计算机应用研究2013，Vol.30Issue(1)：103-106,123,5.DOI:10.3969/j.issn.1001-3695.2013.01.024
基于最大中心间隔的缩放型η-极大熵聚类算法
Maximum center interval and scaling type of η-maximum entropy clustering
摘要
Abstract
In order to control the difference between data, the general way is to scale the data proportionally, and such practices itself do not have any damage to the information of data. However, most algorithms are very sensitive to the scaling data in the cluster analysis and one of the typical algorithms is MEC algorithm. A lot of experiments show that MEC algorithm has failed when the zoom level locating below 10-3 orders of magnitude, and the cluster centers obtained by the algorithm are likely to have consistency clustering. To solve the above problems, this paper introduced the largest center of interval and the scaling factor η to restructure a new objective function, which called the maximum center interval maximum entropy clustering (η-MCS-MEC) algorithm. This algorithm achieved the maximum by adjusting the distance between the center points and controled the division of each cluster by using the scaling factor η effectively, and which avoided the agreement of the clustering centers. Numerical experiments conducting on the UCI standard data sets and artificial data sets show that the proposed algorithm is not sensitive to the changing data and has better robustness.关键词
最大中心间隔/数据缩放/极大熵聚类/中心一致Key words
maximum center interval/ data scaling/ maximum entropy clustering(MEC)/ same center分类
信息技术与安全科学引用本文复制引用
陈爱国,蒋亦樟,钱鹏江..基于最大中心间隔的缩放型η-极大熵聚类算法[J].计算机应用研究,2013,30(1):103-106,123,5.基金项目
国家自然科学基金资助项目(90820002) （90820002）
江苏省自然科学基金资助项目(BK2009067) （BK2009067）
