Description: system clustering algorithm K-means cluster analysis is a basic method is often used squared error criterion function as a cluster criteria, the algorithm in handling large data sets are relatively scalable, high-efficient and has the potential of data parallelism. However, this algorithm depends on the initial value of the options and data input sequence; In addition, when using square error of measurement function and the criteria clustering effect, if the cluster size and shape vary greatly, the square error for the Jc value and minimize the possibility of the emergence of large cluster segmentation clustering phenomenon.
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