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Media type:
E-Article
Title:
Recent advances in cluster analysis
Contributor:
Xu, Rui;
Wunsch, Donald C.
imprint:
Emerald, 2008
Published in:International Journal of Intelligent Computing and Cybernetics
Language:
English
DOI:
10.1108/17563780810919087
ISSN:
1756-378X
Origination:
Footnote:
Description:
<jats:sec><jats:title content-type="abstract-heading">Purpose</jats:title><jats:p>The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Design/methodology/approach</jats:title><jats:p>The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and computational intelligence.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Findings</jats:title><jats:p>The paper reviews the basic issues of cluster analysis and discusses the recent advances of clustering algorithms in scalability, robustness, visualization, irregular cluster shape detection, and so on.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-heading">Originality/value</jats:title><jats:p>The paper presents a comprehensive and systematic survey of cluster analysis and emphasizes its recent efforts in order to meet the challenges caused by the glut of complicated data from a wide variety of communities.</jats:p></jats:sec>