New indices for cluster validity assessment

被引：206

作者：

Kim, M ^{[1
]}

Ramakrishna, RS ^{[1
]}

机构：

[1] Gwangju Inst Sci & Technol, Dept Informat & Commun, Gwangju 500712, South Korea

来源：

PATTERN RECOGNITION LETTERS | 2005年 / 26卷 / 15期

关键词：

unsupervised learning; cluster validity index; clustering algorithm;

D O I：

10.1016/j.patrec.2005.04.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cluster validation is a technique for finding a set of clusters that best fits natural partitions (of given datasets) without the benefit of any a priori class information. A cluster validity index is used to validate the outcome. This paper presents an analysis of design principles implicitly used in defining cluster validity indices and reviews a variety of existing cluster validity indices in the light of these principles. This includes an analysis of their design and performance. Armed with a knowledge of the limitations of existing indices, we proceed to remedy the situation by proposing six new indices. The new indices are evaluated through a series of experiments. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：2353 / 2363

页数：11

共 18 条

[1] NEW LOOK AT STATISTICAL-MODEL IDENTIFICATION
AKAIKE, H
[J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1974, AC19 (06) : 716 - 723
[2] Validity-guided (re)clustering with applications to image segmentation
Bensaid, AM
Hall, LO
Bezdek, JC
Clarke, LP
Silbiger, ML
Arrington, JA
Murtagh, RF
[J]. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 1996, 4 (02) : 112 - 123
[3] Berry MichaelJ., 1997, DATA MINING TECHNIQU
[4] Bezdek J., 1999, FUZZY MODELS ALGORIT
[5] Some new indexes of cluster validity
Bezdek, JC
Pal, NR
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1998, 28 (03): : 301 - 315
[6] Calinski T., 1974, COMMUN STAT, V3, P1, DOI [10.1080/03610927408827101, DOI 10.1080/03610927408827101]
[7] CLUSTER SEPARATION MEASURE
DAVIES, DL
BOULDIN, DW
[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1979, 1 (02) : 224 - 227
[8] Dunn J.C., 1973, J CYBERNETICS, V3, P32, DOI DOI 10.1080/01969727308546046
[9] Halkidi M, 2000, LECT NOTES COMPUT<D>, V1910, P265
[10] Clustering validity assessment: Finding the optimal partitioning of a data set
Halkidi, M
Vazirgiannis, M
[J]. 2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2001, : 187 - 194

← 1 2 →