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Unsupervised learning Clustering methods

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Fuzzy and Neuro-Fuzzy Intelligent Systems

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 47))

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Abstract

In the learning method described in the previous chapter we assume that we have target (desired) output of network for inputs from training data set. In contrast to that, in this chapter we use data set without the desired output of network. Such an approach to network learning without a teacher or supervisor is called an unsupervised method. The effect of that learning are features, regularities and structure of data extraction, and sometimes it is called a method that search for structures of data. For example in biology and medicine, where sets of physical and biochemical measurements define species and diseases, respectively, unsupervised methods are very useful. In this book unsupervised methods will be used to search fuzzy if-then rules. Grouping found by unsupervised methods is frequently referred to as clusters. The cluster is a natural and homogeneous subset of data. The data in each cluster are as similar as possible to each other, and as different (dissimilar) as possible from other cluster’s data.

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Authors and Affiliations

  1. Institute of Electronics, Silesian University of Technology, Akademicka 16, 44-101, Gliwice, Poland

    Professor Ernest Czogała Ph.D., D.Sc. & Professor Jacek Łęski Ph.D., D.Sc.

Authors
  1. Professor Ernest Czogała Ph.D., D.Sc.
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  2. Professor Jacek Łęski Ph.D., D.Sc.
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© 2000 Physica-Verlag Heidelberg

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Czogała, E., Łęski, J. (2000). Unsupervised learning Clustering methods. In: Fuzzy and Neuro-Fuzzy Intelligent Systems. Studies in Fuzziness and Soft Computing, vol 47. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1853-6_4

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  • DOI: https://doi.org/10.1007/978-3-7908-1853-6_4

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-00389-3

  • Online ISBN: 978-3-7908-1853-6

  • eBook Packages: Springer Book Archive

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