JournalArticleRePEc:eee:csdana:v:41:y:2002:i:1:p:59-90Class visualization of high-dimensional data with applications2002DhillonS.InderjitModhaS.DharmendraSpanglerScottW.59-9041Computational Statistics \& Data Analysis1Novemberhttp://www.cs.utexas.edu/~inderjit/public_papers/csda.pdfNo abstract is available for this item.alphaworks, cluster, clustering, dm, visualizationJournalArticlebezdek1997A geometric approach to cluster validity for normal mixtures1997BezdekC.J.LiQ.W.AttikiouzelY.WindhamM.166-1791Soft Computing - A Fusion of Foundations, Methodologies and Applications4#dec#http://dx.doi.org/10.1007/s005000050019We study indices for choosing the correct number of components in a mixture of normal distributions. Previous studies have been confined to indices based wholly on probabilistic models. Viewing mixture decomposition as probabilistic clustering (where the emphasis is on partitioning for geometric substructure) as opposed to parametric estimation enables us to introduce both fuzzy and crisp measures of cluster validity for this problem. We presume the underlying samples to be unlabeled, and use the expectation-maximization (EM) algorithm to find clusters in the data. We test 16 probabilistic, 3 fuzzy and 4 crisp indices on 12 data sets that are samples from bivariate normal mixtures having either 3 or 6 components. Over three run averages based on different initializations of EM, 10 of the 23 indices tested for choosing the right number of mixture components were correct in at least 9 of the 12 trials. Among these were the fuzzy index of Xie-Beni, the crisp Davies-Bouldin index, and two crisp indices that are recent generalizations of Dunn's index.
ER -cluster, evaluation, indexJournalArticlerand1971Objective criteria for the evaluation of clustering methods1971RandW.M.846-85066Journal of the American Statistical Association 336cluster, clustering, criteria, evaluation, index, randBookkaufman1990findingFinding Groups in Data: An Introduction to Cluster Analysis1990KaufmanL.RousseeuwJ.P. ISBN: 1-58133-109-7John Wileyclustering, evaluation, cluster