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Titlebook: Cluster Analysis and Applications; Rudolf Scitovski,Kristian Sabo,Šime Ungar Textbook 2021 The Editor(s) (if applicable) and The Author(s)

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Data Clustering,Whenever we are going to talk about a partition of some set . we will always assume that it consists of subsets as described in Definition 3.1.
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Indexes,Answering this question is one of the most complex problems in Cluster Analysis, and there is a vast literature dealing with it. The usual method for attacking the problem is to check various indicators, simply called ..
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Fuzzy Clustering Problem,In previous chapters, we considered hard clustering methods. In hard clustering approach, each data point either belongs completely to some cluster or not.
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Rudolf Scitovski,Kristian Sabo,Šime UngarClear and precise definitions of basic concepts and notions in clustering, and analysis of their properties.Analysis and implementation of most important methods for searching for optimal partitions.C
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Representatives,properties) of the given set. The quantity commonly used is the well known arithmetic mean of the data. For example, a student’s grade point average can be expressed by arithmetic mean, but it would not be appropriate to represent the average rate of economic growth during several years in such a way.
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Mahalanobis Data Clustering,ems considering problems of grouping data into ellipsoidal clusters. The nature of the problem itself, or some geometrical reasons, often indicates the necessity of finding partitions with ellipsoidal clusters (see Examples in Sect. .).
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Modules and the Data Sets,freely available .-modules are provided. If the data .. are weighted with weights .. > 0, then they are denoted as {.., ..}. If an algorithm requires other modules, these are listed under .. The algorithms can be tested on test-examples or the data sets, also available from the corresponding url-addresses.
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