2D-Visualization of Multi-Dimensional Data by Using Robust Local Principal Component Analysis Based on Alternative c-Means Criterion

DOI 12 References
  • NAKAO Sakuya
    Graduate School of Engineering, Electrical Engineering and Information Science, Osaka Prefecture University
  • HONDA Katsuhiro
    Graduate School of Engineering, Electrical Engineering and Information Science, Osaka Prefecture University
  • NOTSU Akira
    Graduate School of Engineering, Electrical Engineering and Information Science, Osaka Prefecture University

Bibliographic Information

Other Title
  • Alternative c-Means基準を用いたロバストな局所的主成分分析による多次元データの２次元視覚化

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Description

Visualization is a useful approach for knowledge discovery from databases and data mining, since it can support intuitive recognition of intrinsic structures of multi-dimensional observations. Fuzzy c-Varieties (FCV), which is one of the linear fuzzy clustering, performs local principal component analysis, and achieves the lower-dimensional local visualization of multi-dimensional data by using local principal components. Using squared Euclidean distances, however, the results of FCV are often sensitive to noise. In this paper, FCV is extended to a robustified version by using Alternative c-Means criterion, in which a modified distance measure is used based on a robust M-estimation concept. The proposed method is applied to a real world data set in order to construct local 2D-visualization, and the applicability of the visualization approach is investigated through knowledge discovery from the results.

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Details 詳細情報について

  • CRID
    1390282680163821056
  • NII Article ID
    130004491931
  • DOI
    10.3156/jsoft.26.718
  • ISSN
    18817203
    13477986
  • Text Lang
    ja
  • Data Source
    • JaLC
    • Crossref
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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