ATTRIBUTE CLUSTERING IN HIGH DIMENSIONAL FEATURE SPACES

摘要：

In this paper, we will do clustering for the attributes rather than the objects.Like the conventional clustering for objects, the attributes within the same cluster have high similarity, but within different clusters have high dissimilarity.A distance measure for a pair of attributes based on the relative dependency is proposed.An attribute clustering algorithm called Most Neighbors First (MNP) is also proposed to cluster the attributes into a fixed number of groups.An example is also given to illustrate the proposed algorithm.

关键词： Attribute clustering Feature space Dissimilarity measure Rough set

作者: TZUNG-PEI HONG YAN-LIANG LIOU

作者单位: Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan;Depar Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan

会议类型: 国际会议

会议名称: 2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

会议地点: 香港

会议语种:英文

页码: 2286-2289

在线出版日期: 2007-08-19（万方平台首次上网日期，不代表论文的发表时间）