A tree-based algorithm for mining diverse social entities

被引：6

作者：

Braun, Peter ^{[2
]}

Cuzzocrea, Alfredo ^{[1
]}

Leung, Carson K. ^{[2
]}

MacKinnon, Richard Kyle ^{[2
]}

Tanbeer, Syed K. ^{[2
]}

机构：

[1] ICAR CNR, Via P Bucci 41C, I-87036 Arcavacata Di Rende, CS, Italy

[2] Univ Manitoba, Dept Comp Sci, Winnipeg, MB R3T 2N2, Canada

来源：

KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 | 2014年 / 35卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

Data mining; diverse friends; friendship patterns; intelligent information & engineering systems; knowledge based and expert systems; social computing systems; social network analysis; ADOPTION; FRIENDS;

D O I：

10.1016/j.procs.2014.08.102

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2014 The Authors. Published by Elsevier B.V.

引用

页码：223 / 232

页数：10

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