http://www.bibsonomy.org/burst/user/grani/clusterBibSonomy BuRST Feed for /user/grani/cluster2008-10-07T18:47:59+02:00http://www.bibsonomy.org/bibtex/292c03ba02a41f95ae315273939c8daa5/granigrani2006-07-16T10:28:56+02:00sets, feature (ART), (SOFM) validation, networks, clustering clustering, classification, resonance analysis, data neural theory algorithm, salesman benchmark proximity, Adaptive map problem, pattern self-organizing cluster bioinformatics, traveling <span style="color:#555555;">Rui <a href="http://www.bibsonomy.org/author/Xu">Xu</a> und II <a href="http://www.bibsonomy.org/author/Wunsch">Wunsch</a> </span><em>Neural Networks, IEEE Transactions on</em><em>16(3):645--678</em>(<em>2005</em>)Sun Jul 16 10:28:56 CEST 2006Neural Networks, IEEE Transactions on3645--678Survey of clustering algorithms162005sets, feature (ART), (SOFM) validation, networks, clustering clustering, classification, resonance analysis, data neural theory algorithm, salesman benchmark proximity, Adaptive map problem, pattern self-organizing cluster bioinformatics, traveling 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.