﻿ K-Means算法的研究分析及改进

K-Means算法的研究分析及改进Research on K-Means Algorithm Analysis and Improvement

Abstract: Traditional k-means algorithm uses a random number to initialize the cluster center, the main advantage of this method is the ability to quickly produce cluster center initialization, its main drawback is initializing cluster centers may appear in the same a category, leading to excessive iterations, errors and even local optimum clustering result. For the shortcomings of traditional k-means algorithm initial cluster centers, this paper presents the pK-means algorithm, which uses a mathematical geometric distance method for improving the k-means clustering phenomenon of multiple algorithms initial cluster centers unevenly distributed Center appear in the same class cluster phenomenon, this approach avoids k-means clustering algorithm clustering process into local optimization, on the other hand reduces the clustering process repeated iterations. After analyzing and comparing two algorithm experimentally, the article found that the improved algo-rithm is better than the traditional k-means algorithm converges quickly, not easy to fall into local optimum.

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