Cluster ensembles

被引：144

作者：

Ghosh, Joydeep ^{[1
]}

Acharya, Ayan ^{[1
]}

机构：

[1] Univ Texas Austin, Dept Elect & Comp Engn, Austin, TX 78712 USA

来源：

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY | 2011年 / 1卷 / 04期

基金：

美国国家科学基金会;

关键词：

CONSENSUS;

D O I：

10.1002/widm.32

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated clustering, often referred to as the consensus solution. Consensus clustering can be used to generate more robust and stable clustering results compared to a single clustering approach, perform distributed computing under privacy or sharing constraints, or reuse existing knowledge. This paper describes a variety of algorithms that have been proposed to address the cluster ensemble problem, organizing them in conceptual categories that bring out the common threads and lessons learnt while simultaneously highlighting unique features of individual approaches. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 305-315 DOI:10.1002/widm.32

引用

页码：305 / 315

页数：11