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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/39006

    Title: Application of neural networks in cluster analysis
    Other Titles: 類神經網路於群聚分析之應用
    Authors: 蘇木春;Su, Mu-chun;DeClaris, Nicholas;Liu, Ta-kang
    Contributors: 淡江大學電機工程學系
    Date: 1997-10-12
    Issue Date: 2010-04-15 10:49:26 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: How to efficiently specify the “correct” number of clusters from a given multidimensional data set is one of the most fundamental and unsolved problems in cluster analysis. In this paper, we propose a method for automatically discovering the number of clusters and estimating the locations of the centroids of the resulting clusters. This method is based on the interpretation of a self-organizing feature map (SOFM) formed by the given data set. The other difficult problem in cluster analysis is how to choose an appropriate metric for measuring the similarity between a pattern and a cluster centroid. The performance of clustering algorithms greatly depends on the chosen measure of similarity. Clustering algorithms utilizing the Euclidean metric view patterns as a collection of hyperspherical-shaped swarms. Actually, genetic structures of real data sets often exhibit hyperellipsoidal-shaped clusters. In the second part of this paper we present a method of training a single-layer neural network composed of quadratic neurons to cluster data into hyperellipsoidal and/or hyperspherical-shaped swarms. Two data sets are utilized to illustrate the proposed methods.
    Relation: Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on (Volume:1 ), pp.1-6
    DOI: 10.1109/ICSMC.1997.625709
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Proceeding

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