A tree-based algorithm for mining diverse social entities
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Date
2014
Authors
Braun, Peter
Cuzzocrea, Alfredo
Leung, Carson K.
MacKinnon, Richard Kyle
Tanbeer, Syed K.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
DiSE-growth, a tree-based (pattern-growth) algorithm for mining DIverse Social Entities, is proposed and experimentally assessed in this paper. The algorithm makes use of a specialized data structure, called DiSE-tree, for effectively and efficiently representing relevant information on diverse social entities while successfully supporting the mining phase. Diverse entities are popular in a wide spectrum of application scenarios, ranging from linked Web data to Semantic Web and social networks. In all these application scenarios, it has become important to analyze high volumes of valuable linked data and discover those diverse social entities. We complement our analytical contributions by means of an experimental evaluation that clearly shows the benefits of our tree-based diverse social entity mining algorithm.
Description
P. Braun, A. Cuzzocrea, C.K. Leung, R.K. MacKinnon, S.K. Tanbeer. A tree-based algorithm for mining diverse social entities. Procedia Computer Science, 35 (2014), pp. 223-232. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
data mining, diverse friends, friendship patterns, intelligent information & engineering systems, knowledge based and expert systems, social computing systems, social network analysis
Citation
P. Braun, A. Cuzzocrea, C.K. Leung, R.K. MacKinnon, S.K. Tanbeer. A tree-based algorithm for mining diverse social entities. Procedia Computer Science, 35 (2014), pp. 223-232.