Validity measures for the fuzzy cluster analysis of orientations

被引：46

作者：

Hammah, RE

Curran, JH

机构：

[1] Rocsci Inc, Toronto, ON M4E 3B5, Canada

[2] Univ Toronto, Dept Civil Engn, Toronto, ON M5S 1A4, Canada

来源：

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2000年 / 22卷 / 12期

关键词：

fuzzy cluster analysis; cluster analysis; cluster validity index; cluster performance measure; cluster validity indices; cluster performance measures; discontinuities; orientations; spherical data; directional data;

D O I：

10.1109/34.895981

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fuzzy K-means clustering can be applied to the automatic identification of sets in discontinuity data after suitable adaptation of the algorithm. To establish the number of clusters in a data set, modified versions of the validity measures of Gath and Geva, Xie-Beni and Fukuyama-Sugeno are presented in this paper.

引用

页码：1467 / 1472

页数：6

共 11 条

[1]

[Anonymous], Pattern Recognition With Fuzzy Objective Function Algorithms

[2]

DIEDERICHS M, 1996, DIPS USERS GUIDE VER

[3] UNSUPERVISED OPTIMAL FUZZY CLUSTERING [J].