【摘要】 K均值算法的聚类个数K需指定,聚类结果与数据输入顺序相关,而且易受孤立点影响.针对这些缺陷,首先以实验的方式证明了找到最优的初始质心是K-MEANS算法有效的条件,对局部版的微粒群优化算法(PSO)进行了改进,利用其局部搜索的功能查找到K均值算法的最优初始质心和存在的孤立点,克服了K均值算法的这些缺陷。更多还原

【Abstract】 K-means algorithm has some deficiencies.The number K must be pointed and its effectiveness liable to be effected by isolated data and the input sequence of data.To solve these deficiencies,data experiments were done to find the precondition of K-means effectiveness,which is finding the best initial core.Then a new algorithm that base on PSO is composed to find the initial core and the outlier.Through these,the disadvantages of K-means were solved.更多还原