Topology-based hierarchical clustering of self-organizing maps.

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  • Author(s): Taşdemir K;Taşdemir K; Milenov P; Tapsall B
  • Source:
    IEEE transactions on neural networks [IEEE Trans Neural Netw] 2011 Mar; Vol. 22 (3), pp. 474-85.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Institute of Electrical and Electronics Engineers Country of Publication: United States NLM ID: 101211035 Publication Model: Print Cited Medium: Internet ISSN: 1941-0093 (Electronic) Linking ISSN: 10459227 NLM ISO Abbreviation: IEEE Trans Neural Netw Subsets: MEDLINE
    • Publication Information:
      Original Publication: New York, NY : Institute of Electrical and Electronics Engineers, c1990-
    • Subject Terms:
      Algorithms* ; Artificial Intelligence* ; Maps as Topic* ; Neural Networks, Computer*; Pattern Recognition, Automated/*methods; Computer Simulation ; Image Processing, Computer-Assisted/methods ; Principal Component Analysis/methods
    • Abstract:
      A powerful method in the analysis of datasets where there are many natural clusters with varying statistics such as different sizes, shapes, density distribution, overlaps, etc., is the use of self-organizing maps (SOMs). However, further processing tools, such as visualization and interactive clustering, are often necessary to capture the clusters from the learned SOM knowledge. A recent visualization scheme (CONNvis) and its interactive clustering utilize the data topology for SOM knowledge representation by using a connectivity matrix (a weighted Delaunay graph), CONN. In this paper, we propose an automated clustering method for SOMs, which is a hierarchical agglomerative clustering of CONN. We determine the number of clusters either by using cluster validity indices or by prior knowledge on the datasets. We show that, for the datasets used in this paper, data-topology-based hierarchical clustering can produce better partitioning than hierarchical clustering based solely on distance information.
    • Publication Date:
      Date Created: 20110302 Date Completed: 20120116 Latest Revision: 20191210
    • Publication Date:
      20231215
    • Accession Number:
      10.1109/TNN.2011.2107527
    • Accession Number:
      21356611