%0 Journal Article
%T Structure-based Entropy Clustering Algorithm for Heterogeneous Data<br>异构数据的结构熵聚类算法
%A LI Zhi-hua
%A GU yan
%A CHEN Meng-tao
%A WANG Shi-tong
%A CHEN Xiu-hong
%A <br>李志华
%A 顾言
%A 陈孟涛
%A 王士同
%A 陈秀宏
%J 计算机科学
%D 2011
%I 
%X The dissimilarity measure and clustering approach about the heterogeneous dataset were studied, and a struclure-based entropy clustering SEC algorithm was presented in this paper. Data often do appear in homogeneous groups,the SEC utilizes these structural information to improve the clustering accuracy. Unlike the distribution of numeric data,nominal data are often unbalancedly distributed,whose distribution are often unrelated with their distance measure,due to the above, a new structural information-based entropy computing technology was proposed. By mining the clues in structural information, constructing the weight implying the different distribution information of nominal and numeric attributes, the SEC can automatically identifies the initial locations and number of cluster centriods, and exhibits its robustness to initialization and no iteration in algorithm. Experimental results comparing with other references demonstrate that the proposed method has promising performance.
%K Heterogeneous data
%K Dissimilarity measure
%K Clustering clue
%K Structural entropy
%K Clustering algorithm<br>异构数据，相异性度量，聚类线索，结构嫡，聚类算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=6A2BDDF46373EBE9B36C557BDEE6E29D&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=0B39A22176CE99FB&sid=73579BC9CFB2D787&eid=0584DB487B4581F4&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=11