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Qing Deng

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Proceedings Article | 26 February 2010 Paper

Study of improved adaptive mountain clustering algorithm

Qing Deng, Jianhui Liu

Proceedings Volume 7546, 75461L (2010) https://doi.org/10.1117/12.855081

KEYWORDS: Statistical analysis, Data centers, Data mining, Iris, Dimension reduction, Computer simulations, Electronics engineering, Digital image processing, Current controlled current source, Particle filters

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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.

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