Cluster Analysis

Wolfgang Karl Härdle and Leopold Simar
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics

Chapter Chapter 13 in Applied Multivariate Statistical Analysis, 2015, pp 385-405 from Springer

Abstract: Abstract The next two chapters address classification issues from two varying perspectives. When considering groups of objects in a multivariate data set, two situations can arise. Given a data set containing measurements on individuals, in some cases we want to see if some natural groups or classes of individuals exist, and in other cases, we want to classify the individuals according to a set of existing groups. Cluster analysis develops tools and methods concerning the former case, that is, given a data matrix containing multivariate measurements on a large number of individuals (or objects), the objective is to build some natural sub-groups or clusters of individuals. This is done by grouping individuals that are “similar” according to some appropriate criterion.

Keywords: Proximity Measure; Hierarchical Algorithm; Agglomerative Algorithm; Conditional Frequency; Boston Housing (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-662-45171-7_13

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