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The word of a cluster which implies a bunch of things of the same kind or a group of similar things is becoming popular now in a variety of scientific fields. This word has different technical meanings in different disciplines, but what we study in this book is cluster analysis or data clustering which is a branch in data analysis and implies a bundle of algorithms for unsupervised classification. For this reason we use the term of cluster analysis, data clustering, unsupervised classification exchangeably and we frequently call it simply clustering, as many researchers do.
Classification problems have been considered in both classical and Bayesian statistics [83, 30], and also in studies in neural networks [10]. A major part of studies has been devoted to supervised classification in which a number of classes of objects are given beforehand and an arbitrary observation should be allocated into one of the classes. In other words, a set of classification rules should be derived from a set of mathematical assumptions and the given classes. Unsupervised classification problems are also mentioned or considered in most textbooks at the same time (e.g., [83, 10, 30]). In an unsupervised classification problem, no predefined classes are given but data objects or individuals should form a number of groups so that distances between a pair of objects within a group should be relatively small and those between different groups should be relatively large.
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© 2008 Springer-Verlag Berlin Heidelberg
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Miyamoto, S., Ichihashi, H., Honda, K. (2008). Introduction. In: Algorithms for Fuzzy Clustering. Studies in Fuzziness and Soft Computing, vol 229. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78737-2_1
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DOI: https://doi.org/10.1007/978-3-540-78737-2_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78736-5
Online ISBN: 978-3-540-78737-2
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