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Publications (10 of 49) Show all publications
Torra, V., Guillen, M. & Santolino, M. (2018). Continuous m-dimensional distorted probabilities. Information Fusion, 44, 97-102
Open this publication in new window or tab >>Continuous m-dimensional distorted probabilities
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 44, p. 97-102Article in journal (Refereed) Published
National Category
Engineering and Technology Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14763 (URN)10.1016/j.inffus.2017.12.004 (DOI)000435059200008 ()2-s2.0-85039798077 (Scopus ID)
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-07-06Bibliographically approved
Alcantud, J. C. & Torra, V. (2018). Decomposition theorems and extension principles for hesitant fuzzy sets. Information Fusion, 41, 48-56
Open this publication in new window or tab >>Decomposition theorems and extension principles for hesitant fuzzy sets
2018 (English)In: Information Fusion, ISSN 1566-2535, E-ISSN 1872-6305, Vol. 41, p. 48-56Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Hesitant fuzzy set, Cut set, Decomposition theorem, Representation theorem, Extension principle
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14605 (URN)10.1016/j.inffus.2017.08.005 (DOI)000417662100006 ()2-s2.0-85027534671 (Scopus ID)
Available from: 2017-12-28 Created: 2017-12-28 Last updated: 2018-02-01Bibliographically approved
Bae, J., Ventocilla, E., Riveiro, M. & Torra, V. (2018). On the Visualization of Discrete Non-additive Measures. In: Torra V, Mesiar R, Baets B (Ed.), Aggregation Functions in Theory and in Practice AGOP 2017: . Paper presented at 9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017 (pp. 200-210). Springer
Open this publication in new window or tab >>On the Visualization of Discrete Non-additive Measures
2018 (English)In: Aggregation Functions in Theory and in Practice AGOP 2017 / [ed] Torra V, Mesiar R, Baets B, Springer, 2018, p. 200-210Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2018
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 581
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15590 (URN)10.1007/978-3-319-59306-7_21 (DOI)000432811600021 ()2-s2.0-85019989762 (Scopus ID)978-3-319-59306-7 (ISBN)978-3-319-59305-0 (ISBN)
Conference
9th International Summer School on Aggregation Functions (AGOP), Skövde, Sweden, June 19-22, 2017
Available from: 2018-06-14 Created: 2018-06-14 Last updated: 2018-10-02Bibliographically approved
Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. (2017). A survey of graph-modification techniques for privacy-preserving on networks. Artificial Intelligence Review, 47(3), 341-366
Open this publication in new window or tab >>A survey of graph-modification techniques for privacy-preserving on networks
2017 (English)In: Artificial Intelligence Review, ISSN 0269-2821, E-ISSN 1573-7462, Vol. 47, no 3, p. 341-366Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Privacy, k-Anonymity, Randomization, Social networks, Graphs
National Category
Engineering and Technology Computer Systems
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
urn:nbn:se:his:diva-13465 (URN)10.1007/s10462-016-9484-8 (DOI)000394302100003 ()2-s2.0-84973106733 (Scopus ID)
Available from: 2017-03-31 Created: 2017-03-31 Last updated: 2018-06-11Bibliographically approved
Torra, V. (2017). Data Privacy: Foundations, New Developments and the Big Data Challenge. Springer
Open this publication in new window or tab >>Data Privacy: Foundations, New Developments and the Big Data Challenge
2017 (English)Book (Other academic)
Place, publisher, year, edition, pages
Springer, 2017. p. 269
Series
Studies in Big Data, ISSN 2197-6503, E-ISSN 2197-6511 ; 28
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14571 (URN)10.1007/978-3-319-57358-8 (DOI)978-3-319-57356-4 (ISBN)978-3-319-57358-8 (ISBN)
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-02-01Bibliographically approved
Torra, V. (2017). Entropy for non-additive measures in continuous domains. Fuzzy sets and systems (Print), 324, 49-59
Open this publication in new window or tab >>Entropy for non-additive measures in continuous domains
2017 (English)In: Fuzzy sets and systems (Print), ISSN 0165-0114, E-ISSN 1872-6801, Vol. 324, p. 49-59Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Entropy, KL-divergence, Non-additive measures
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14115 (URN)10.1016/j.fss.2016.10.001 (DOI)000408022500005 ()2-s2.0-85006022255 (Scopus ID)
Available from: 2017-09-14 Created: 2017-09-14 Last updated: 2018-03-20Bibliographically approved
Torra, V. (2017). Fuzzy microaggregation for the transparency principle. Journal of Applied Logic, 23, 70-80
Open this publication in new window or tab >>Fuzzy microaggregation for the transparency principle
2017 (English)In: Journal of Applied Logic, ISSN 1570-8683, E-ISSN 1570-8691, Vol. 23, p. 70-80Article in journal (Refereed) Published
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. 

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
Data privacy, Fuzzy c-means, Fuzzy clustering, Application of fuzzy sets theory, Microaggregation, Transparency attacks
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
urn:nbn:se:his:diva-14057 (URN)10.1016/j.jal.2016.11.007 (DOI)000406728700007 ()2-s2.0-85007109704 (Scopus ID)
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2018-06-11Bibliographically approved
Casas-Roma, J., Herrera-Joancomarti, J. & Torra, V. (2017). k-Degree Anonymity And Edge Selection: Improving Data Utility In Large Networks. Knowledge and Information Systems, 50(2), 447-474
Open this publication in new window or tab >>k-Degree Anonymity And Edge Selection: Improving Data Utility In Large Networks
2017 (English)In: Knowledge and Information Systems, ISSN 0219-1377, E-ISSN 0219-3116, Vol. 50, no 2, p. 447-474Article in journal (Refereed) Published
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.

National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science; INF303 Information Security
Identifiers
urn:nbn:se:his:diva-13356 (URN)10.1007/s10115-016-0947-7 (DOI)000393661500004 ()2-s2.0-85010032093 (Scopus ID)
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2018-06-11Bibliographically approved
Aliahmadipour, L., Torra, V. & Eslami, E. (2017). On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data. In: Vicenç Torra, Anders Dahlbom & Yasuo Narukawa (Ed.), Fuzzy sets, rough sets, multisets and clustering: Part I (pp. 157-168). Springer
Open this publication in new window or tab >>On Hesitant Fuzzy Clustering and Clustering of Hesitant Fuzzy Data
2017 (English)In: Fuzzy sets, rough sets, multisets and clustering: Part I / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, p. 157-168Chapter in book (Refereed)
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.

Place, publisher, year, edition, pages
Springer, 2017
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 671
Keywords
Hesitant fuzzy sets, Data mining, Clustering algorithm, Fuzzy clustering
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-13363 (URN)10.1007/978-3-319-47557-8_10 (DOI)000413720000011 ()2-s2.0-85009957968 (Scopus ID)978-3-319-47556-1 (ISBN)978-3-319-47557-8 (ISBN)
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2018-06-11Bibliographically approved
Torra, V., Narukawa, Y. & Dahlbom, A. (2017). On this book: Clustering, multisets, rough sets and fuzzy sets. In: Vicenç Torra, Anders Dahlbom & Yasuo Narukawa (Ed.), Fuzzy sets, rough sets, multisets and clustering: (pp. 1-5). Springer
Open this publication in new window or tab >>On this book: Clustering, multisets, rough sets and fuzzy sets
2017 (English)In: Fuzzy sets, rough sets, multisets and clustering / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, p. 1-5Chapter in book (Other academic)
Abstract [en]

This chapter gives an overview of the content of this book, and links them with the work of Prof. Sadaaki Miyamoto, to whom this book is dedicated.

Place, publisher, year, edition, pages
Springer, 2017
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 671
Keywords
Hesitant fuzzy sets, Data mining, Clustering algorithm, Fuzzy clustering
National Category
Computer Sciences
Research subject
Natural sciences; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-13362 (URN)10.1007/978-3-319-47557-8_1 (DOI)000413720000002 ()2-s2.0-85009982265 (Scopus ID)978-3-319-47556-1 (ISBN)978-3-319-47557-8 (ISBN)
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2018-03-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-0368-8037

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