%0 Journal Article
%T Research on K-means initial clustering center optimal algorithm<br>K-means初始聚类中心优化算法研究
%A MAO Shao-yang
%A LI Ken-li
%A <br>毛韶阳
%A 李肯立
%J 重庆邮电大学学报(自然科学版)
%D 2007
%I 
%X Since the dependence of K-means algorithm on the initial center may sink into the local minimum, the experimental result of the multi-seed clustering based on the density function method and merging small cluster obviously surpasses that of K-means clustering. Every iteration of this algorithm inclines to discover hyper-sphere cluster. The algorithm has better clustering ability especially for irregular and extendable clusters.
%K 聚类分析
%K K-means
%K 多中心聚类算法
%K 小类合并
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=01BA20E8BA813E1908F3698710BBFEFEE816345F465FEBA5&cid=96E6E851B5104576C2DD9FC1FBCB69EF&jid=5C2694A2E5629ECD6B59D7B28C6937AD&aid=339C00CDF950E8B1&yid=A732AF04DDA03BB3&vid=2A8D03AD8076A2E3&iid=E158A972A605785F&sid=B941678158018439&eid=F10601728A1E9BEA&journal_id=1673-825X&journal_name=重庆邮电大学学报(自然科学版)&referenced_num=0&reference_num=6