Study of improved adaptive mountain clustering algorithm

Qing Deng; Jianhui Liu

doi:10.1117/12.855081

26 February 2010 Study of improved adaptive mountain clustering algorithm

Qing Deng, Jianhui Liu

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 75461L (2010) https://doi.org/10.1117/12.855081
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

Qing Deng and Jianhui Liu "Study of improved adaptive mountain clustering algorithm", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 75461L (26 February 2010); https://doi.org/10.1117/12.855081

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