Publisher： Trans Tech Publications
E-ISSN： 1662-7482|2015|713|1808-1812
ISSN： 1660-9336
Source： Applied Mechanics and Materials, Vol.2015, Iss.713, 2015-02, pp. : 1808-1812
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Abstract
K-means is a classic algorithm of clustering analysis and widely applied to various data mining fields. Traditional K-means algorithm selects the initial centroids randomly, so the clustering result will be affected by the noise points, and the clustering result is not stable. For this problem, this paper proposed a k-means algorithm based on density outlier detection. The algorithm firstly detected the outliers with the density model and avoided selecting outliers as the initial cluster centers. After clustering the non outlier, according to distance of the outlier to each centroids, the algorithm distributed the outliers to the corresponding clustering. The algorithm effectively reduced the influence of outliers to K-means and improved the accuracy of clustering result. The experimental result demonstrated that this algorithm can effectively improve the accurate rate and stability of the clustering.
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