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.

A tree-based algorithm for mining diverse social entities

Cuzzocrea Alfredo;
2014

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.
Data mining
Diverse friends
Friendship patterns
Intelligent information & engineering systems
Knowledge based and expert
Computer Science (all)
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/312646
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