Survey of clustering algorithms

被引：3618

作者：

Xu, R ^{[1
]}

Wunsch, D ^{[1
]}

机构：

[1] Univ Missouri, Dept Elect & Comp Engn, Rolla, MO 65409 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2005年 / 16卷 / 03期

基金：

美国国家科学基金会;

关键词：

adaptive resonance theory (ART); clustering; clustering algorithm; cluster validation; neural networks; proximity; self-organizing feature map (SOFM);

D O I：

10.1109/TNN.2005.845141

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

引用

页码：645 / 678

页数：34

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