On distance measures for the fuzzy K-means algorithm for joint data

Citation
Re. Hammah et Jh. Curran, On distance measures for the fuzzy K-means algorithm for joint data, ROCK MECH R, 32(1), 1999, pp. 1-27
Citations number
25
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Geological Petroleum & Minig Engineering
Journal title
ROCK MECHANICS AND ROCK ENGINEERING
ISSN journal
0723-2632 → ACNP
Volume
32
Issue
1
Year of publication
1999
Pages
1 - 27
Database
ISI
SICI code
0723-2632(199901/03)32:1<1:ODMFTF>2.0.ZU;2-7
Abstract
The analysis of data collected on rock discontinuities often requires that the data be separated into joint sets or groups. A statistical tool that fa cilitates the automatic identification of groups of clusters of observation s in a data set is cluster analysis. The fuzzy K-means cluster technique ha s been successfully applied to the analysis of joint survey data. As is the case with all clustering algorithms, the results of an analysis performed with the fuzzy K-means algorithm for discontinuity data are highly dependen t on the distance metric employed in the analysis. This paper explores the significant issues surrounding the choice and use of various distance measu res for clustering joint survey data. It also proposes an analogue of the M ahalanobis distance norm (used for data in Euclidean space) for clustering spherical data. Sample applications showing the greater flexibility and pow er of the new distance measure over the originally proposed distance metric for spherical data are given in the paper.