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Clustering Algorithms For Intelligent Web
College of Information Technology, UAE University, United Arab Emirates.
College of Information Technology, UAE University, United Arab Emirates.
College of Information Technology, UAE University, United Arab Emirates.ORCID-id: 0000-0002-7312-9089
2016 (Engelska)Ingår i: International Journal of Computational Complexity and Intelligent Algorithmslgorithms, ISSN 2048-4720, Vol. 1, nr 1, s. 1-22Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

Detecting users and data in the web is an important issue as the web is changing and new information is created every day. In this paper we will discuss six different clustering algorithms that are related to the intelligent web. These algorithms will help us to identify groups of interest in the web, which is very necessary in or- der to perform certain actions on specific group such as targeted advertisement. The algorithms under consideration are: Single-Link algorithm, Average-Link algorithm, Minimum-Spanning-Tree Single-Link algorithm, K-means algorithm, ROCK algorithm and DBSCAN algorithm. These algorithms are categorized into three groups: Hierarchical, Partitional and Density-based algorithms. We will show how each algorithm works and discuss their advantages and disadvantages. We will compare these algorithms to each others and discuss their ability to handle social web data which are of large datasets and high dimensionality. Finally a case study related to using clustering in social networks will be discussed.

Ort, förlag, år, upplaga, sidor
InderScience Publishers, 2016. Vol. 1, nr 1, s. 1-22
Nyckelord [en]
Algorithms, clustering, intelligent web, social networks
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:his:diva-12067DOI: 10.1504/IJCCIA.2016.077462OAI: oai:DiVA.org:his-12067DiVA, id: diva2:913716
Tillgänglig från: 2016-03-22 Skapad: 2016-03-22 Senast uppdaterad: 2018-01-10Bibliografiskt granskad

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