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A modified mountain clustering algorithm

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Abstract

In this paper, we modify the mountain method and then create a modified mountain clustering algorithm. The proposed algorithm can automatically estimate the parameters in the modified mountain function in accordance with the structure of the data set based on the correlation self-comparison method. This algorithm can also estimate the number of clusters based on the proposed validity index. As a clustering tool to a grouped data set, the modified mountain algorithm becomes a new unsupervised approximate clustering method. Some examples are presented to demonstrate this algorithm’s simplicity and effectiveness and the computational complexity is also analyzed.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their helpful comments and suggestions to improve the presentation of the paper This work was supported in part by the National Science Council of Taiwan, under Grant NSC-89-2213-E-033-057

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Authors and Affiliations

  1. Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan, 32023, ROC

    Miin-Shen Yang

  2. Department of Information Management, Kun Shan University of Technology, Yung-Kang, Tainan, Taiwan, 71023, ROC

    Kuo-Lung Wu

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  1. Miin-Shen Yang
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  2. Kuo-Lung Wu
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Correspondence to Miin-Shen Yang.

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Yang, MS., Wu, KL. A modified mountain clustering algorithm. Pattern Anal Applic 8, 125–138 (2005). https://doi.org/10.1007/s10044-005-0250-9

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  • DOI: https://doi.org/10.1007/s10044-005-0250-9

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