K-means Clustering Algorithm to Optimize the Initial Center Point
Describes an algorithm which initial cluster centers can be optimized. The improvement is to isolate point for special treatment, reduce outlier sensitive issue, combines the distance and density to select the appropriate initial focal point, so that improves the clustering accu-racy, in order to improve the efficiency of the algorithm, the algorithm in the process of calculating the distance between the stored data objects. The experimental results prove that the improved clustering algorithm can achieve better results and higher efficiency of the algo-rithm.
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