Finding Groups in Data: An Introduction to Cluster Analysis by Leonard Kaufman, Peter J. Rousseeuw

Finding Groups in Data: An Introduction to Cluster Analysis



Finding Groups in Data: An Introduction to Cluster Analysis book




Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw ebook
Page: 355
ISBN: 0471735787, 9780471735786
Format: pdf
Publisher: Wiley-Interscience


Finding Groups in Data: An Introduction to Cluster Analysis (Wiley. The algorithm is called Clara in R, and is described in chapter 3 of Finding Groups in Data: An Introduction to Cluster Analysis. Unlike the evaluation of supervised classifiers, which can be conducted using well-accepted objective measures and procedures, Relative measures try to find the best clustering structure generated by a clustering algorithm using different parameter values. Hoboken, New Jersey: Wiley; 2005. There is a specific k-medoids clustering algorithm for large datasets. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability 1967, 1:281-297. Clustering can be considered the most important unsupervised learning problem; so, as every other problem of this kind, it deals with finding a structure in a collection of unlabeled data. Publications on Spatial Database and Spatial Data Mining at UMN . Leonard Kaufman and Peter Rousseeuw (2005), Finding Groups in Data: An Introduction to Cluster Analysis, Wiley Series in Probability and Statistics, 337 p. Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to cluster analysis. The grouping process implements a clustering methodology called "Partitioning Around Mediods" as detailed in chapter 2 of L. Knowledge Discovery and Data Mining (PAKDD. Cluster analysis, the most widely adopted unsupervised learning process, organizes data objects into groups that have high intra-group similarities and inter-group dissimilarities without a priori information.