%0 Journal Article
%T Robust Clustering Based on Finite Mixtures t Distribution<br>基于有限混合多变量t分布的鲁棒聚类算法
%A YU Cheng-Wen
%A GUO Lei
%A <br>余成文
%A 郭雷
%J 计算机科学
%D 2007
%I 
%X Providing protection against outlier in clustering data is a difficult problem for mixtures models fitting.In this paper,we consider the fitting of mixtures t distributions alternative to mixtures normal distributions for multi-component gauss data with background noise,to improve the robustness of fitting.We propose two modified versions of EM algorithm and integrate them with a model selection criterion respectively,then we get two robust clustering algorithms which can avoid the drawbacks of traditional algorithms(EM/ECM) for solving mixtures t models-highly dependent on initialization and may converge to the boundary of the parameter space,and can also select the number of clusters component automatically by a combined component annihilation strategy.Experiment results show the contrast among different algorithms and demonstrate the effectiveness of our algorithms.
%K Outlier
%K Robust clustering
%K Mixtures t distribution
%K Expectation maximization
%K Model selection criterion<br>局外点
%K 鲁棒聚类
%K 混合t模型
%K 期望最大化算法
%K 模型选择准则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=1A6F7A5C8452B4EA0255C68EE5A0F25D&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=94C357A881DFC066&sid=6235172E4DDBA109&eid=23104246A5FCFCEF&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=9