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Finding Groups in Data: An Introduction to Cluster Analysis Leonard Kaufman, Peter J. Rousseeuw
Publisher: Wiley-Interscience

4 Centralisation of wage bargaining. 3 Collectivisation of wage bargaining. Finding groups in data, an introduction to cluster analysis. Applied multivariate statistical analysis, (3rd ed.). SIAM J Comput 1982, 11(4):721-736. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. The identification of the cluster centroid or the most representative [voucher or barcode] .. 5.1 Direct government involvement in wage setting. 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. 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. 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. The grouping process implements a clustering methodology called "Partitioning Around Mediods" as detailed in chapter 2 of L. The exponential accumulation of DNA and protein sequencing data has demanded efficient tools for the comparison, analysis, clustering, and classification of novel and annotated sequences [1,2]. 5 Wage bargaining coordination and government involvement. 18 Our data provide information from 1995 and 2006 for 23 European countries, plus the US and Japan. Kaufman L, Rousseeuw P: Finding Groups in Data: An Introduction to Cluster Analysis. Because the clustering method failed to separate the patient data into groups by obvious traditional physiological definitions these results confirm our hypothesis that clustering would find meaningful patterns of data that were otherwise impossible to physiologically discern or classify using traditional clinical definitions.