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  • 1.
    Abril, Daniel
    et al.
    IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain / UAB, Universitat Autónoma de Barcelona, Bellaterra, Spain.
    Navarro-Arribas, Guillermo
    DEIC, Dep. Enginyeria de la Informació i de les Comunicacions, UAB, Universitat Autònoma de Barcelona, Bellaterra, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain.
    Spherical Microaggregation: Anonymizing Sparse Vector Spaces2015Ingår i: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 49, s. 28-44Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets.

  • 2.
    Abril, Daniel
    et al.
    IIIA, Institut d'Investigació en Intel·ligència Artificial, CSIC, Consejo Superior de Investigaciones Científicas, Campus UAB s/n, Bellaterra, Spain / UAB, Universitat Autònoma de Barcelona, Campus UAB s/n, Bellaterra, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA, Institut d'Investigació en Intel·ligència Artificial, CSIC, Consejo Superior de Investigaciones Científicas, Campus UAB s/n, Bellaterra, Spain.
    Navarro-Arribas, Guillermo
    DEIC, Dep. Enginyeria de la Informació i de les Comunicacions, UAB, Universitat Autònoma de Barcelona, Campus UAB s/n, Bellaterra, Spain.
    Supervised Learning Using a Symmetric Bilinear Form for Record Linkage2015Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 26, s. 144-153Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk.

    In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions.

  • 3.
    Alcantud, Jose Carlos R.
    et al.
    BORDA Research Unit and Multidisciplinary Institute of Enterprise (IME), University of Salamanca, Salamanca, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Decomposition theorems and extension principles for hesitant fuzzy sets2018Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 41, s. 48-56Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We prove a decomposition theorem for hesitant fuzzy sets, which states that every typical hesitant fuzzy set on a set can be represented by a well-structured family of fuzzy sets on that set. This decomposition is expressed by the novel concept of hesitant fuzzy set associated with a family of hesitant fuzzy sets, in terms of newly defined families of their cuts. Our result supposes the first representation theorem of hesitant fuzzy sets in the literature. Other related representation results are proven. We also define two novel extension principles that extend crisp functions to functions that map hesitant fuzzy sets into hesitant fuzzy sets.

  • 4.
    Aliahmadipour, Laya
    et al.
    Faculty of Mathematics and Computer, Department of Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Eslami, Esfandiar
    Faculty of Mathematics and Computer, Department of Mathematics, Shahid Bahonar University of Kerman, Kerman, Iran.
    On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data2017Ingår i: Fuzzy sets, rough sets, multisets and clustering: Part I / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, s. 157-168Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Since the notion of hesitant fuzzy set was introduced, some clustering algorithms have been proposed to cluster hesitant fuzzy data. Beside of hesitation in data, there is some hesitation in the clustering (classification) of a crisp data set. This hesitation may be arise in the selection process of a suitable clustering (classification) algorithm and initial parametrization of a clustering (classification) algorithm. Hesitant fuzzy set theory is a suitable tool to deal with this kind of problems. In this study, we introduce two different points of view to apply hesitant fuzzy sets in the data mining tasks, specially in the clustering algorithms.

  • 5.
    Aliahmadipour, Laya
    et al.
    Department of Applied Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Eslami, Esfandiar
    Department of Pure Mathematics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.
    Eftekhari, Mahdi
    Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
    A definition for hesitant fuzzy partitions2016Ingår i: International Journal of Computational Intelligence Systems, ISSN 1875-6891, E-ISSN 1875-6883, Vol. 9, nr 3, s. 497-505Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.

  • 6.
    Armengol, Eva
    et al.
    CSIC - Spanish Council for Scientific Research, IIIA - Artificial Intelligence Research Institute, Bellaterra, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Generalization-Based k-Anonymization2015Ingår i: Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21–23, 2015: Proceedings / [ed] Vicenç Torra & Yasuo Narukawa, Springer, 2015, s. 207-218Konferensbidrag (Refereegranskat)
    Abstract [en]

    Microaggregation is an anonymization technique consistingon partitioning the data into clusters no smaller thankelements andthen replacing the whole cluster by its prototypical representant. Mostof microaggregation techniques work on numerical attributes. However,many data sets are described by heterogeneous types of data, i.e., nu-merical and categorical attributes. In this paper we propose a new mi-croaggregation method for achieving a compliantk-anonymous maskedfile for categorical microdata based on generalization. The goal is to builda generalized description satisfied by at leastkdomain objects and toreplace these domain objects by the description. The way to constructthat generalization is similar that the one used in growing decision trees.Records that cannot be generalized satisfactorily are discarded, thereforesome information is lost. In the experiments we performed we prove thatthe new approach gives good results.

  • 7.
    Armengol, Eva
    et al.
    IIIA - Artificial Intelligence Research Institute, CSIC - Spanish Council for Scientific Research, Catalonia, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Partial Domain Theories for Privacy2016Ingår i: Modeling Decisions for Artificial Intelligence: 13th International Conference, MDAI 2016 Sant Julià de Lòria, Andorra, September 19–21, 2016, Proceedings, Springer, 2016, s. 217-226Konferensbidrag (Refereegranskat)
    Abstract [en]

    Generalization and Suppression are two of the most used techniques to achieve k-anonymity. However, the generalization concept is also used in machine learning to obtain domain models useful for the classification task, and the suppression is the way to achieve such generalization. In this paper we want to address the anonymization of data preserving the classification task. What we propose is to use machine learning methods to obtain partial domain theories formed by partial descriptions of classes. Differently than in machine learning, we impose that such descriptions be as specific as possible, i.e., formed by the maximum number of attributes. This is achieved by suppressing some values of some records. In our method, we suppress only a particular value of an attribute in only a subset of records, that is, we use local suppression. This avoids one of the problems of global suppression that is the loss of more information than necessary.

  • 8.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Mellin, Jonas
    Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Högskolan i Skövde, Institutionen för informationsteknologi.
    Ståhl, Niclas
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Complex Data Analysis2019Ingår i: Data science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 157-169Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Data science applications often need to deal with data that does not fit into the standard entity-attribute-value model. In this chapter we discuss three of these other types of data. We discuss texts, images and graphs. The importance of social media is one of the reason for the interest on graphs as they are a way to represent social networks and, in general, any type of interaction between people. In this chapter we present examples of tools that can be used to extract information and, thus, analyze these three types of data. In particular, we discuss topic modeling using a hierarchical statistical model as a way to extract relevant topics from texts, image analysis using convolutional neural networks, and measures and visual methods to summarize information from graphs.

  • 9.
    Bae, Juhee
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Ventocilla, Elio
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Riveiro, Maria
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On the Visualization of Discrete Non-additive Measures2018Ingår i: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, s. 200-210Konferensbidrag (Refereegranskat)
    Abstract [en]

    Non-additive measures generalize additive measures, and have been utilized in several applications. They are used to represent different types of uncertainty and also to represent importance in data aggregation. As non-additive measures are set functions, the number of values to be considered grows exponentially. This makes difficult their definition but also their interpretation and understanding. In order to support understability, this paper explores the topic of visualizing discrete non-additive measures using node-link diagram representations.

  • 10.
    Casas-Roma, Jordi
    et al.
    Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Herrera-Joancomarti, Jordi
    Department of Information and Communications Engineering, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    k-Degree Anonymity And Edge Selection: Improving Data Utility In Large Networks2017Ingår i: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 50, nr 2, s. 447-474Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The problem of anonymization in large networks and the utility of released data are considered in this paper. Although there are some anonymization methods for networks, most of them cannot be applied in large networks because of their complexity. In this paper, we devise a simple and efficient algorithm for k-degree anonymity in large networks. Our algorithm constructs a k-degree anonymous network by the minimum number of edge modifications. We compare our algorithm with other well-known k-degree anonymous algorithms and demonstrate that information loss in real networks is lowered. Moreover, we consider the edge relevance in order to improve the data utility on anonymized networks. By considering the neighbourhood centrality score of each edge, we preserve the most important edges of the network, reducing the information loss and increasing the data utility. An evaluation of clustering processes is performed on our algorithm, proving that edge neighbourhood centrality increases data utility. Lastly, we apply our algorithm to different large real datasets and demonstrate their efficiency and practical utility.

  • 11.
    Casas-Roma, Jordi
    et al.
    Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain.
    Herrera-Joancomartí, Jordi
    Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A survey of graph-modification techniques for privacy-preserving on networks2017Ingår i: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 47, nr 3, s. 341-366Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.

  • 12.
    Ghorbani, Ali
    et al.
    University of New Brunswick, Canada.
    Torra, VincençHögskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.Hişil, HüseyinYasar University, Turkey.Miri, AliRyerson University, Canada.Koltuksuz, AhmetYasar University, Turkey.Zhang, JieNanyang Technological University, Singapore.Sensoy, MuratOzyegin University, Turkey.García-Alfaro, JoaquínTélécom SudParis, France.Zincir, IbrahimYasar University, Turkey.
    2015 Thirteenth Annual Conference on Privacy, Security and Trust2015Proceedings (redaktörskap) (Refereegranskat)
  • 13.
    Halas, Radomir
    et al.
    Palacký University Olomouc, Faculty of Science, Department of Algebra and Geometry, Olomouc, Czech Republic.
    Mesiar, Radko
    Palacký University Olomouc, Faculty of Science, Department of Algebra and Geometry, Olomouc, Czech Republic / Department of Mathematics and Descriptive Geometry, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Bratislava, Slovakia.
    Pocs, Jozef
    Palacký University Olomouc, Faculty of Science, Department of Algebra and Geometry, Olomouc, Czech Republic / Mathematical Institute, Slovak Academy of Sciences, Košice, Slovakia.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A note on some algebraic properties of discrete Sugeno integrals2019Ingår i: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 355, s. 110-120Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Based on the link between Sugeno integrals and fuzzy measures, we discuss several algebraic properties of discrete Sugeno integrals. We recall that the composition of Sugeno integrals is again a Sugeno integral, and that each Sugeno integral can be obtained as a composition of binary Sugeno integrals. In particular, we discuss the associativity, dominance, commuting and bisymmetry of Sugeno integrals.

  • 14.
    Koloseni, David
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Department of Mathematics, University of Dar es salaam, Tanzania.
    Helldin, Tove
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Absolute and relative preferences in AHP-like matrices2018Ingår i: Data Science and Knowledge Engineering for Sensing Decision Support: Proceedings of the 13th International FLINS Conference (FLINS 2018) / [ed] Jun Liu, Jie Lu, Yang Xu, Luis Martinez, Etienne E Kerre, SINGAPORE: World Scientific Publishing Co. Pte. Ltd. , 2018, Vol. 11, s. 260-267Konferensbidrag (Refereegranskat)
    Abstract [en]

    The Analytical Hierarchy Process (AHP) has been extensively used to interview experts in order to find the weights of the criteria. We call AHP-like matrices relative preferences of weights. In this paper we propose another type of matrix that we call a absolute preference matrix. They are also used to find weights, and we propose that they can be applied to find the weights of weighted means and also of the Choquet integral.

  • 15.
    Kuchaki Rafsanjani, Marjan
    et al.
    Department of Computer Science, Shadid Bahonar University of Kerman, Iran.
    Aliahmadipour, Laya
    Department of Computer Science, Shadid Bahonar University of Kerman, Iran.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An application of Hesitant Fuzzy Sets to Elect an Efficient Cluster Head in Ad Hoc Networks2016Konferensbidrag (Övrigt vetenskapligt)
  • 16.
    Livraga, Giovanni
    et al.
    Università degli Studi di Milano, Crema, Italy.
    Torra, VicençHögskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.Aldini, AlessandroUniversity of Urbino, Urbino, Italy.Martinelli, FabioIIT-CNR, Pisa, Italy.Suri, NeerajTU Darmstadt, Germany.
    Data Privacy Management and Security Assurance: 11th International Workshop, DPM 2016 and 5th International Workshop, QASA 2016, Heraklion, Crete, Greece, September 26-27, 2016, Proceedings2016Proceedings (redaktörskap) (Refereegranskat)
  • 17. Narukawa, Yasuo
    et al.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    New results on hesitant fuzzy sets: score functions2015Ingår i: The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings, MDAI - HiS , 2015Konferensbidrag (Refereegranskat)
  • 18.
    Rodríguez, R. M.
    et al.
    Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain.
    Bedregal, B.
    Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Brazil.
    Bustince, H.
    Department of Automatic and Computation and Institute of Smart Cities, Public University of Navarra, Pamplona, Spain.
    Dong, Y. C.
    Business School, Sichuan University, Chengdu, China.
    Farhadinia, B.
    Department of Mathematics, Quchan University of Advanced Technology, Iran.
    Kahraman, C.
    Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey.
    Martínez, L.
    Department of Computer Science, University of Jaén, Jaén, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Xu, Y. J.
    Business School, Hohai University, Nanjing, China.
    Xu, Z. S.
    Business School, Sichuan University, Chengdu, China.
    Herrera, F.
    Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain / Faculty of Computing and Information Technology, King Abdulaziz University, North Jeddah, Saudi Arabia.
    A Position and Perspective Analysis of Hesitant Fuzzy Sets on Information Fusion in Decision Making: Towards High Quality Progress2016Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 29, s. 89-97Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The necessity of dealing with uncertainty in real world problems has been a long-term research challenge which has originated different methodologies and theories. Recently, the concept of Hesitant Fuzzy Sets (HFSs) has been introduced to model the uncertainty that often appears when it is necessary to establish the membership degree of an element and there are some possible values that make to hesitate about which one would be the right one. Many researchers have paid attention on this concept who have proposed diverse extensions, relationships with other types of fuzzy sets, different types of operators to compute with this type of information, applications on information fusion and decision-making, etc.

    Nevertheless, some of these proposals are questionable, because they are straightforward extensions of previous works or they do not use the concept of HFSs in a suitable way. Therefore, this position paper studies the necessity of HFSs and provides a discussion about current proposals including a guideline that the proposals should follow and some challenges of HFSs.

  • 19.
    Rodríguez, Rosa M.
    et al.
    University of Granada, Granada, Spain.
    Martínez, Luis
    University of Jaén, Jaén, Spain.
    Herrera, Francisco
    University of Granada, Granada, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A Review of Hesitant Fuzzy Sets: Quantitative and Qualitative Extensions2016Ingår i: Fuzzy Logic in Its 50th Year: New Developments, Directions and Challenges / [ed] Cengiz Kahraman, Uzay Kaymak, Adnan Yazici, Springer, 2016, s. 109-128Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    Abstract [en]

    Since the concept of fuzzy set was introduced, different extensions and generalizations have been proposed to manage the uncertainty in different problems. This chapter is focused in a recent extension so-called hesitant fuzzy set. Many researchers have paid attention on it and have proposed different extensions both in quantitative and qualitative contexts. Several concepts, basic operations and its extensions are revised in this chapter.

  • 20.
    Said, Alan
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Data Science: An Introduction2019Ingår i: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 1-6Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    This chapter gives a general introduction to data science as a concept and to the topics covered in this book. First, we present a rough definition of data science, and point out how it relates to the areas of statistics, machine learning and big data technologies. Then, we review some of the most relevant tools that can be used in data science ranging from optimization to software. We also discuss the relevance of building models from data. The chapter ends with a detailed review of the structure of the book.

  • 21.
    Salas, Julian
    et al.
    Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Center for Cybersecurity Research of Catalonia (CYBERCAT), Barcelona, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A General Algorithm for k-anonymity on Dynamic Databases2018Ingår i: Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2018 International Workshops, DPM 2018 and CBT 2018, Barcelona, Spain, September 6-7, 2018, Proceedings / [ed] Joaquin Garcia-Alfaro, Jordi Herrera-Joancomartí, Giovanni Livraga, Ruben Rios, Cham: Springer, 2018, Vol. 11025, s. 407-414Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this work we present an algorithm for k-anonymization of datasets that are changing over time. It is intended for preventing identity disclosure in dynamic datasets via microaggregation. It supports adding, deleting and updating records in a database, while keeping k-anonymity on each release. We carry out experiments on database anonymization. We expected that the additional constraints for k-anonymization of dynamic databases would entail a larger information loss, however it stays close to MDAV's information loss for static databases. Finally, we carry out a proof of concept experiment with directed degree sequence anonymization, in which the removal or addition of records, implies the modification of other records.

  • 22.
    Salas, Julian
    et al.
    Department of Computer Engineering and Mathematics, Universitat Rovira i Virgili, Tarragona, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Improving the characterization of P-stability for applications in network privacy2016Ingår i: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 206, s. 109-114Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Recently, we have found that the concept of P-stability has interesting applications in network privacy. In the context of Online Social Networks it may be used for obtaining a fully polynomial randomized approximation scheme for graph masking and measuring disclosure risk. Also by using the characterization for P-stable sequences from Jerrum, McKay and Sinclair (1992) it is possible to obtain optimal approximations for the problem of k-degree anonymity. In this paper, we present results on P-stability considering the additional restriction that the degree sequence must not intersect the edges of an excluded graph X, improving earlier results on P-stability. As a consequence we extend the P-stable classes of scale-free networks from Torra et al. (2015), obtain an optimal solution for k-anonymity and prove that all the known conditions for P-stability are sufficient for sequences to be graphic. (C) 2016 Elsevier B.V. All rights reserved.

  • 23.
    Salas, Julián
    et al.
    Internet Interdisciplinary Institute (IN3), CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Megías, David
    Internet Interdisciplinary Institute (IN3), CYBERCAT-Center for Cybersecurity Research of Catalonia, Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    SwapMob: Swapping trajectories for mobility anonymization2018Ingår i: Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2018, Valencia, Spain, September 26–28, 2018, Proceedings / [ed] Josep Domingo-Ferrer, Fransisco Montes, Springer, 2018, s. 331-346Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mobility data mining can improve decision making, from planning transports in metropolitan areas to localizing services in towns. However, unrestricted access to such data may reveal sensible locations and pose safety risks if the data is associated to a specific moving individual. This is one of the many reasons to consider trajectory anonymization. Some anonymization methods rely on grouping individual registers on a database and publishing summaries in such a way that individual information is protected inside the group. Other approaches consist of adding noise, such as differential privacy, in a way that the presence of an individual cannot be inferred from the data. In this paper, we present a perturbative anonymization method based on swapping segments for trajectory data (SwapMob). It preserves the aggregate information of the spatial database and at the same time, provides anonymity to the individuals. We have performed tests on a set of GPS trajectories of 10,357 taxis during the period of Feb. 2 to Feb. 8, 2008, within Beijing. We show that home addresses and POIs of specific individuals cannot be inferred after anonymizing them with SwapMob, and remark that the aggregate mobility data is preserved without changes, such as the average length of trajectories or the number of cars and their directions on any given zone at a specific time.

  • 24.
    Salas, Julián
    et al.
    IIIA-CSIC, Consejo Superior de Investigaciones Científicas, Institut d’Investigació en Intelligència Artificial, Campus Universitat Autònoma de Barcelona, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA-CSIC, Consejo Superior de Investigaciones Científicas, Institut d’Investigació en Intelligència Artificial, Campus Universitat Autònoma de Barcelona, Spain.
    Graphic sequences, distances and k-degree anonymity2015Ingår i: Discrete Applied Mathematics, ISSN 0166-218X, E-ISSN 1872-6771, Vol. 188, nr 1, s. 25-31Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper we study conditions to approximate a given graph by a regular one. We obtain optimal conditions for a few metrics such as the edge rotation distance for graphs, the rectilinear and the Euclidean distance over degree sequences. Then, we require the approximation to have at least kk copies of each value in the degree sequence, this is a property proceeding from data privacy that is called kk-degree anonymity.

    We give a sufficient condition in order for a degree sequence to be graphic that depends only on its length and its maximum and minimum degrees. Using this condition we give an optimal solution of kk-degree anonymity for the Euclidean distance when the sum of the degrees in the anonymized degree sequence is even. We present algorithms that may be used for obtaining all the mentioned anonymizations.

  • 25.
    Saleh, Emran
    et al.
    Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
    Valls, Aida
    Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
    Moreno, Antonio
    Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Tarragona, Spain.
    Romero-Aroca, Pedro
    Ophthalmic Service, University Hospital Sant Joan de Reus, Institut d’Investigació Sanitària Pere Virgili (IISPV), Universitat Rovira i Virgili, Reus, Spain.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Bustince, Humberto
    Departamento de Automàtica y Computación, Universidad Pública de Navarra, Institute of Smart Cities, Pamplona, Spain.
    Learning Fuzzy Measures for Aggregation in Fuzzy Rule-Based Models2018Ingår i: Modeling Decisions for Artificial Intelligence: 15th International Conference, MDAI 2018, Mallorca, Spain, October 15–18, 2018, Proceedings / [ed] Vicenç Torra, Yasuo Narukawa, Isabel Aguiló, Manuel González-Hidalgo, Springer, 2018, s. 114-127Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fuzzy measures are used to express background knowledge of the information sources. In fuzzy rule-based models, the rule confidence gives an important information about the final classes and their relevance. This work proposes to use fuzzy measures and integrals to combine rules confidences when making a decision. A Sugeno $$\lambda $$ -measure and a distorted probability have been used in this process. A clinical decision support system (CDSS) has been built by applying this approach to a medical dataset. Then we use our system to estimate the risk of developing diabetic retinopathy. We show performance results comparing our system with others in the literature. 

  • 26.
    Senavirathne, Navoda
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Approximating Robust Linear Regression With An Integral Privacy Guarantee2018Ingår i: 2018 16th Annual Conference on Privacy, Security and Trust (PST) / [ed] Kieran McLaughlin, Ali Ghorbani, Sakir Sezer, Rongxing Lu, Liqun Chen, Robert H. Deng, Paul Miller, Stephen Marsh, Jason Nurse, IEEE, 2018, s. 85-94Konferensbidrag (Refereegranskat)
    Abstract [en]

    Most of the privacy-preserving techniques suffer from an inevitable utility loss due to different perturbations carried out on the input data or the models in order to gain privacy. When it comes to machine learning (ML) based prediction models, accuracy is the key criterion for model selection. Thus, an accuracy loss due to privacy implementations is undesirable. The motivation of this work, is to implement the privacy model "integral privacy" and to evaluate its eligibility as a technique for machine learning model selection while preserving model utility. In this paper, a linear regression approximation method is implemented based on integral privacy which ensures high accuracy and robustness while maintaining a degree of privacy for ML models. The proposed method uses a re-sampling based estimator to construct linear regression model which is coupled with a rounding based data discretization method to support integral privacy principles. The implementation is evaluated in comparison with differential privacy in terms of privacy, accuracy and robustness of the output ML models. In comparison, integral privacy based solution provides a better solution with respect to the above criteria.

  • 27.
    Senavirathne, Navoda
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Maynooth University Hamilton Institute, Kildare, Ireland.
    Integrally private model selection for decision trees2019Ingår i: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 83, s. 167-181Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Privacy attacks targeting machine learning models are evolving. One of the primary goals of such attacks is to infer information about the training data used to construct the models. “Integral Privacy” focuses on machine learning and statistical models which explain how we can utilize intruder's uncertainty to provide a privacy guarantee against model comparison attacks. Through experimental results, we show how the distribution of models can be used to achieve integral privacy. Here, we observe two categories of machine learning models based on their frequency of occurrence in the model space. Then we explain the privacy implications of selecting each of them based on a new attack model and empirical results. Also, we provide recommendations for private model selection based on the accuracy and stability of the models along with the diversity of training data that can be used to generate the models. 

  • 28.
    Stokes, Klara
    et al.
    Universitat Oberta de Catalunya, Barcelona, Spain.
    Torra, Vicenç
    Consejo Superior de Investigaciones Científicas (CSIC), Spain.
    Multiple releases of k-anonymous data sets and k-anonymous relational databases2012Ingår i: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, ISSN 0218-4885, Vol. 20, nr 6, s. 839-853Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In data privacy, the evaluation of the disclosure risk has to take into account the fact that several releases of the same or similar information about a population are common. In this paper we discuss this issue within the scope of k-anonymity. We also show how this issue is related to the publication of privacy protected databases that consist of linked tables. We present algorithms for the implementation of k-anonymity for this type of data.

  • 29.
    Stokes, Klara
    et al.
    Universitat Rovira i Virgili, Tarragona, Spain.
    Torra, Vicenç
    Universitat Autònoma de Barcelona (UAB), Spain.
    On some clustering approaches for graphs2011Ingår i: Fuzzy Systems (FUZZ), 2011, IEEE conference proceedings, 2011, s. 409-415Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we discuss some tools for graph perturbation with applications to data privacy. We present and analyse two different approaches. One is based on matrix decomposition and the other on graph partitioning. We discuss these methods and show that they belong to two traditions in data protection: noise addition/microaggregation and k-anonymity.

  • 30.
    Stokes, Klara
    et al.
    Universitat Rovira i Virgili, Tarragona, Spain.
    Torra, Vicenç
    Universitat Autònoma de Barcelona (UAB), Spain.
    On the Relationship Between Clustering and Coding Theory2012Ingår i: 2012 IEEE International Conference on Fuzzy Systems: Brisbane, Australia (June 10-15, 2012) / [ed] Hussein Abbass, Daryl Essam & Ruhul Sarker, IEEE conference proceedings, 2012, s. Article number 6250783-Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we discuss the relations between clustering and error correcting codes. We show that clustering can be used for constructing error correcting codes. We review the previous works found in the literature about this issue, and propose a modification of a previous work that can be used for code construction from a set of proposed codewords.

  • 31.
    Stokes, Klara
    et al.
    Universitat Rovira i Virgili, Tarragona, Catalonia, Spain.
    Torra, Vicenç
    IIIA, Institut d’Investigació en Intel ligència Artificial CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Catalonia, Spain.
    Reidentification and k-anonymity: a model for disclosure risk in graphs2012Ingår i: Soft Computing - A Fusion of Foundations, Methodologies and Applications, ISSN 1432-7643, E-ISSN 1433-7479, Vol. 16, nr 10, s. 1657-1670Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modeling the anonymity of a database table and we prove that n-confusion is a generalization of k-anonymity. After a short survey on the different available definitions of k-anonymity for graphs we provide a new definition for k-anonymous graph, which we consider to be the correct definition. We provide a description of the k-anonymous graphs, both for the regular and the non-regular case. We also introduce the more flexible concept of (k, l)-anonymous graph. Our definition of (k, l)-anonymous graph is meant to replace a previous definition of (k, l)-anonymous graph, which we here prove to have severe weaknesses. Finally, we provide a set of algorithms for k-anonymization of graphs.

  • 32.
    Torra, Vicenc
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On the Analysis of Utility and Risk for Masked Data in Big Data: A Small Data Analysis2018Ingår i: Frontiers in Artificial Intelligence and Applications: Artificial Intelligence Research and Development / [ed] Zoe Falomir, Karina Gibert, Enric Plaza, IOS Press , 2018, Vol. 308, s. 200-209Konferensbidrag (Refereegranskat)
    Abstract [en]

    Data privacy studies methods to ensure that disclosure of sensitive information does not take place. Masking methods are applied to databases prior to their release so that intruders cannot access sensitive information. Masking methods modify the data reducing its quality. Information loss measures have been defined to evaluate in what extent data is still useful for particular analysis. In the case of big data, masking data and evaluating its utility is a complex problem. In this paper we focus on information loss measurement and we explore if we can estimate or give bounds of information loss for large data sets using only random subsets of the whole data set.

  • 33.
    Torra, Vicenc
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Some properties of Choquet integral based probability functions2015Ingår i: Acta et Commentationes Universitatis Tartuensis de Mathematica, ISSN 1406-2283, E-ISSN 2228-4699, Vol. 19, nr 1, s. 35-47Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Choquet integral permits us to integrate a function with respect to a non-additive measure. When the measure is additive it corresponds to the Lebesgue integral. This integral was used recently to define families of probability-density functions. They are the exponential family of Choquet integral (CI) based class-conditional probability-density functions, and the exponential family of Choquet– Mahalanobis integral (CMI) based class-conditional probability-density functions. The latter being a generalization of the former, and also a generalization of the normal distribution.

    In this paper we study some properties of these distributions, and study the application of a few normality tests.

  • 34.
    Torra, Vicenc
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Transparency and Disclosure Risk in Data Privacy2015Ingår i: Proceedings of the Workshops of the EDBT/ICDT 2015 Joint Conference (EDBT/ICDT) / [ed] Peter M. Fischer, Gustavo Alonso, Marcelo Arenas, Floris Geerts, 2015, s. 246-Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    k-Anonymity and differential privacy can be considered examples of Boolean definitions of disclosure risk. In contrast, record linkage and uniqueness are examples of quantitative measures of risk. Record linkage is a powerful approach because it can model different types of scenarios in which an adversary attacks a protected database with some information and background knowledge. Transparency holds in data privacy when data is published together with details on their processing. This includes the data protection method used and its parameters. Intruders can use this information to improve their attacks. Specific record linkage algorithms can be defined to take into account this information, and to define more accurate disclosure risk measures. Machine learning and optimization techniques also permits us to increase the  effectiveness of record linkage algorithms. This talk will be focused on disclosure risk measures based on record linkage. We will describe how we can improve the performance of the algorithms under the transparency principle, as well as using machine learning and optimization techniques.

  • 35.
    Torra, Vicenc
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Transparency and microaggregation2015Ingår i: UNECE SDC 2015, 2015Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Transparency has an important effect on disclosure risk. In general, masking methods have to be evaluated taking into account that intruders can use all available information to attack the data. When the masking method as well as their parameters are disclosed, this information can also be used by an intruder. In this talk we will review results on the effects of transparency in disclosure risk assessment for microdata giving special emphasis to microaggregation.

  • 36.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    A fuzzy microaggregation algorithm using fuzzy c-means2015Ingår i: Artificial Intelligence Research and Development / [ed] Eva Armengol, Dionís Boixader, Francisco Grimaldo, IOS Press, 2015, s. 214-223Konferensbidrag (Refereegranskat)
    Abstract [en]

    Masking methods are used in data privacy to avoid the disclosure of sensitive information. Microaggregation is a perturbative masking method that has been proven effective. Data masked using microaggregation can be attacked when the intruder has information of the masking method and the parameters used. Publishing this information is usual under the transparency principle. Fuzzy microaggregation was introduced a few years ago to avoid this type of transparency attacks. In this paper we propose a new simpler method for microaggregation based on fuzzy c-means. We discuss the effectiveness of the approach. One of the advantages of this approach is its computational complexity.

  • 37.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Choquet integral: distributions and decisions2016Ingår i: 83rd meeting of the European Working Group on Multicriteria Decision Aiding! / [ed] Núria Agell, Barcelona: ESADE, Ramon Llull University , 2016Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

    Choquet integrals integrate functions with respect to fuzzy measures. From a mathematical point of view these integrals generalize the Lebesgue integrals when the measures are additive. From a point of view of aggregation functions, one of the relevant aspects is that they generalize the weighted mean and the OWA. Choquet integrals have been successfully used in decision making problems when there are interactions between criteria. In this setting we can learn or identify the measures from a set of decisions. This fact seems to indicate that we can consider data as generated from distributions based on the Choquet integral. We will present some results on these types of distributions and on their generalizations.  

  • 38.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Data Privacy: Foundations, New Developments and the Big Data Challenge2017Bok (Övrigt vetenskapligt)
  • 39.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Derivation of Priorities and Weights for Set-Valued Matrices Using the Geometric Mean Approach2015Ingår i: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 29, nr 5, s. 500-513Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Priorities are essential in the analytic hierarchy process (AHP). Several approaches have been proposed to derive priorities in the framework of the AHP. Priorities correspond to the weights in the weighted mean as well as in other aggregation operators as the ordered weighted averaging (OWA) operators, and the quasi-arithmetic means.

    Derivation of priorities for the AHP typically starts by eliciting a preference matrix from an expert and then using this matrix to obtain the vector priorities. For consistent matrices, the vector of priorities is unique. Nevertheless, it is usual that the matrix is not consistent. In this case, different methods exist for extracting this vector from the matrix.

    This article introduces a method for this purpose when the cells of the matrix are not a single value but a set of values. That is, we have a set-valued preference matrix. We discuss the relation of this type of matrices and hesitant fuzzy preference relations.

  • 40.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Entropy for non-additive measures in continuous domains2017Ingår i: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 324, s. 49-59Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In a recent paper we introduced a definition of f-divergence for non-additive measures. In this paper we use this result to give a definition of entropy for non-additive measures in a continuous setting. It is based on the KL divergence for this type of measures. We prove some properties and show that we can use it to find a measure satisfying the principle of minimum discrimination.

  • 41.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Fuzzy microaggregation for the transparency principle2017Ingår i: Journal of Applied Logic, ISSN 1570-8683, E-ISSN 1570-8691, Vol. 23, s. 70-80Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Microaggregation has been proven to be an effective method for data protection in the areas of Privacy Preserving Data Mining (PPDM) and Statistical Disclosure Control (SDC). This method consists of applying a clustering method to the data set to be protected, and then replacing each of the data by the cluster representative. In this paper we propose a new method for microaggregation based on fuzzy clustering. This new approach has been defined with the main goal of being nondeterministic on the assignment of cluster centers to the original data, and at the same time being simple in its definition. Being nondeterministic permits us to overcome some of the attacks standard microaggregation suffers. 

  • 42.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On fuzzy c-means and membership based clustering2015Ingår i: Advances in Computational Intelligence: 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, June 10-12, 2015: Proceedings, Part I / [ed] Ignacio Rojas, Gonzalo Joya & Andreu Catala, Springer, 2015, s. 597-607Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fuzzyc" role="presentation">c-means is one of the most well known fuzzy clustering algorithms. It is usually solved using an iterative algorithm. This algorithm does not ensure that the solution is the global optimum. In this paper we study the distribution of values of the objective function of fuzzyc" role="presentation">c

    -means.

    We also propose a new fuzzy clustering method related to fuzzy c-means. The method presumes that the shape of the membership function is known and can be calculated from the cluster centers, which are the only results of the clustering algorihm.

  • 43.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    On the selection of m for Fuzzy c-Means2015Ingår i: Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology / [ed] José M. Alonso, Humberto Bustince & Marek Reformat, Paris: Atlantis Press , 2015, s. 1571-1577Konferensbidrag (Refereegranskat)
    Abstract [en]

    Fuzzy c-means is a well known fuzzy clustering algorithm. It is an unsupervised clustering algorithmthat permits us to build a fuzzy partition from data. The algorithm depends on a parameter m whichcorresponds to the degree of fuzziness of the solution. Large values of m will blur the classes andall elements tend to belong to all clusters. The solutionsof the optimization problem depend on theparameter m. That is, different selections of m willtypically lead to different partitions. In this paper we study and compare the effect ofthe selection of m obtained from the fuzzy c-means.

  • 44.
    Torra, Vicenç
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Scala: from a functional programming perspective: An Introduction to the Programming Language2016Bok (Övrigt vetenskapligt)
    Abstract [en]

    This book gives an introduction to the programming language Scala. It presents it from a functional programming perspective. The book explains with detail functional programming and recursivity, and includes chapters on lazy and eager evaluation, streams, higher-order functions (including map, fold, reduce, and aggregate), and algebraic data types. The book also describes the object-oriented aspects of Scala, as they are a fundamental part of the language. In addition, the book includes a chapter on parallelism in Scala, giving an overview of the actor model.

  • 45.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Aliahmadipour, Laya
    Departement of Computer Science Shahid Bahonar University of Kerman, Iran.
    Dahlbom, Anders
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters2016Ingår i: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2016, s. 524-530Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this paper we discuss how three types of fuzzy partitions can be used to describe the results of three types of cluster structures. Standard fuzzy partitions are suitable for centroid based clusters, and I-fuzzy partitions for clusters represented by segments or lines (e.g., c-varieties). In this paper, we introduce hesitant fuzzy partitions. They are suitable for clusters defined by sets of centroids. Because of that, we show that they are useful for hierarchical clustering. We also establish the relationship between hesitant fuzzy partitions and I-fuzzy partitions.

  • 46.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Guillen, Montserrat
    Department of Econometrics, Riskcenter-IREA, University of Barcelona, Spain.
    Santolino, Miguel
    Department of Econometrics, Riskcenter-IREA, University of Barcelona, Spain.
    Continuous m-dimensional distorted probabilities2018Ingår i: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 44, s. 97-102Artikel i tidskrift (Refereegranskat)
  • 47.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Jonsson, Annie
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Navarro‐Arribas, Guillermo
    Universitat Autònoma de Barcelona, Spain / Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain.
    Salas, Julián
    Center for Cybersecurity Research of Catalonia (CYBERCAT), Spain / Universitat Oberta de Catalunya (UOC), Barcelona, Spain.
    Synthetic generation of spatial graphs2018Ingår i: International Journal of Intelligent Systems, ISSN 0884-8173, E-ISSN 1098-111X, Vol. 32, nr 12, s. 2364-2378Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Graphs can be used to model many different types of interaction networks, for example, online social networks or animal transport networks. Several algorithms have thus been introduced to build graphs according to some predefined conditions. In this paper, we present an algorithm that generates spatial graphs with a given degree sequence. In spatial graphs, nodes are located in a space equiped with a metric. Our goal is to define a graph in such a way that the nodes and edges are positioned according to an underlying metric. More particularly, we have constructed a greedy algorithm that generates nodes proportional to an underlying probability distribution from the spatial structure, and then generates edges inversely proportional to the Euclidean distance between nodes. The algorithm first generates a graph that can be a multigraph, and then corrects multiedges. Our motivation is in data privacy for social networks, where a key problem is the ability to build synthetic graphs. These graphs need to satisfy a set of required properties (e.g., the degrees of the nodes) but also be realistic, and thus, nodes (individuals) should be located according to a spatial structure and connections should be added taking into account nearness.

  • 48.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Karlsson, Alexander
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Steinhauer, H. Joe
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Berglund, Stefan
    Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi.
    Artificial Intelligence2019Ingår i: Data Science in Practice / [ed] Alan Said, Vicenç Torra, Springer, 2019, s. 9-26Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    This chapter gives a brief introduction to what artificial intelligence is. We begin discussing some of the alternative definitions for artificial intelligence and introduce the four major areas of the field. Then, in subsequent sections we present these areas. They are problem solving and search, knowledge representation and knowledge-based systems, machine learning, and distributed artificial intelligence. The chapter follows with a discussion on some ethical dilemma we find in relation to artificial intelligence. A summary closes this chapter.

  • 49.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, Yasuo
    Distances on non-additive measures using the numerical Choquet integrale2015Ingår i: The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings, MDAI - HiS , 2015Konferensbidrag (Refereegranskat)
  • 50.
    Torra, Vicenç
    et al.
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Narukawa, YasuoToho Gakuen, Tokyo, Japan.
    Modeling Decisions for Artificial Intelligence: 12th International Conference, MDAI 2015, Skövde, Sweden, September 21-23, 2015, Proceedings2015Proceedings (redaktörskap) (Refereegranskat)
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