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Proceedings Paper

Study of improved adaptive mountain clustering algorithm
Author(s): Qing Deng; Jianhui Liu
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Paper Abstract

In the problem of determining number of clustering and initial cluster centers, the mountain clustering algorithm was a simple and effective algorithm, it was a kind of clustering algorithm which could cluster sample set approximately and also could be used as the basis of other cluster analysis, which could provide initial cluster centers for other clustering algorithms. The improved algorithm of it was subtractive clustering, which had a great improvement in solving the problem of low efficiency of large sample set for mountain clustering, but its adaptability was not perfect. Therefore, put forward the regionalism adaptable mountain clustering algorithm, which based on the traditional mountain clustering algorithm divided sample set into regions and chose sample points of the largest weight to calculate their best initial value. Experimental results showed that the algorithm had stronger adaptability and accuracy of clustering, moreover speed was improved.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75461L (26 February 2010); doi: 10.1117/12.855081
Show Author Affiliations
Qing Deng, Liaoning Technical Univ. (China)
Jianhui Liu, Liaoning Technical Univ. (China)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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