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Springer, Berlin, Heidelberg

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10.1016/0165-3806(82)90158-4

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A K-Means Optimization Algorithm Based on Relative Core Cluster

Authors: Gang Liu, Shaobin Huang, Haiyan Chang,

Publish Date: 2012
Volume: , Issue:, Pages: 385-391
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Abstract

With the rapid development of the technology of cluster analysis, people have proposed a lot of clustering algorithms, such as the K-means clustering algorithm which is simple, low complexity and has been used widely, and it has been the improved object or base for many other algorithms. This paper presents a K-means optimization algorithm based on relative core cluster -RCBK-means. The algorithm is based on the core group, uses the center of the relative core cluster of the data set as the initial center of the K-means algorithm, thus avoiding the local optimization problem of the clustering results which caused by selecting the initial center randomly of the classic K-means algorithm, and improving the algorithm results effectively.


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