Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data

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Nowadays, clustering is a popular tool for exploratory data analysis, with one technique being K-means clustering. Determining the appropriate number of clusters is a significant problem in K-means clustering because the results of the k-means technique depend on different numbers of clusters. Automatic determination of the appropriate number of clusters in a K-means clustering application is often needed in advance as an input parameter to the K-means algorithm. We propose a new method for automatic determination of the appropriate number of clusters using an extended co-occurrence matrix technique called a tri-co-occurrence matrix technique for multispectral imagery in the pre-clustering steps. The proposed method was tested using a dataset from a known number of clusters. The experimental results were compared with ground truth images and evaluated in terms of accuracy, with the numerical result of the tri-co-occurrence providing an accuracy of 84.86%. The results from the tests confirmed the effectiveness of the proposed method in finding the appropriate number of clusters and were compared with the original co-occurrence matrix technique and other algorithms.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 95(5), 1256-1263, 2012-05-01

    The Institute of Electronics, Information and Communication Engineers

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各種コード

  • NII論文ID(NAID)
    10030942570
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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