A Tree-based Algorithm for Mining Diverse Social Entities

• Published in: Procedia computer science Vol. 35; pp. 223 - 232
• Main Authors: Braun, Peter, Cuzzocrea, Alfredo, Leung, Carson K., MacKinnon, Richard Kyle, Tanbeer, Syed K.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2014
• Subjects: Data mining; diverse friends; friendship patterns; intelligent information & engineering systems; knowledge based and expert systems; social computing systems; social network analysis; intelligent information & engineering systems; knowledge based and expert systems; social network analysis; diverse friends; Data mining; social computing systems; friendship patterns
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2014.08.102

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.