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

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

Cluster analysis is a collection of statistical methods, which identifies groups of samples that behave similarly or show similar characteristics. Audience The following groups will find this book a valuable tool and reference: applied statisticians; engineers and scientists using data analysis; researchers in pattern recognition, artificial intelligence, machine learning, and data mining; and applied mathematicians. Mirkin B: Mathematical Classification and Clustering. Kaufman L, Rousseeuw PJ: Finding Groups in Data. Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. Finding groups in data: An introduction to cluster analysis. Cluster analysis is called Q-analysis (finding distinct ethnic groups using data about believes and feelings1), numerical taxonomy (biology), classification analysis (sociology, business, psychology), typology2 and so on. The stated problem is quite difficult, in particular for microarrays, since the inferred prediction must be Kaufman L, Rousseeuw PJ: Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons; 1990. An Introduction to Cluster Analysis. The inference of the number of clusters in a dataset, a fundamental problem in Statistics, Data Analysis and Classification, is usually addressed via internal validation measures. Nevertheless, using an integrative analysis of gene expression microarray data from three untreated (no chemotherapy) ER- breast cancer cohorts (a total of 186 patients) [3,8,10] and a novel feature selection method [11], it was possible to identify a seven-gene immune response expression module associated with good prognosis,. Instructors can also use it as a textbook for an introductory course in cluster analysis or as source material for a graduate-level introduction to data mining. Introduction 1.1 What is cluster analysis? Hierarchical Cluster Analysis Some Basics and Algorithms 1. This suggests that at least part Kaufman L, Rousseeuw P: Finding Groups in Data: An introduction to Cluster Analysis. This cluster technique has the benefit over the more commonly used k-means and k-medoid cluster analysis, and other grouping methods, in that it allocates a membership value (in the form of a probability value) for each possible construct-cluster pairing rather than simply assigning a construct to a single cluster, thereby the membership of items to more than one group could be Kaufman L, Rousseeuw PJ: Finding groups in data: an introduction to data analysis.