Standardization and weighting of variables for the fuzzy K-means clustering of discontinuity data

4th North American Rock Mechanics Symposium, NARMS 2000

Volumn , Issue , 2000, Pages 659-666

  Hammah, R E ^a   Curran, J H ^b  

^a Colder Associates Limited ^*  (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; ROCK MECHANICS;

CLUSTER RECOVERY; CLUSTER STRUCTURE; FUZZY K MEANS CLUSTERING; FUZZY K-MEANS; IMPORTANCE WEIGHTS; MEASUREMENT SCALE; NOISE VARIABLES; VARIABLE WEIGHTING;

STANDARDIZATION;

EID: 2142843743     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bezdek, J. C, 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. New York; Plenum.
2. DeSarbg, W. S., Carroll, J. D., Clark, I,. A. 1984. Synthesized Clustering: A Method for Amalgamating Alternative Clustering Bases with Differential Weighting of Variables. Psycho-metrika 49 (1): 57-78.
3. Everitt, B. 1980. Cluster Analysis. New York: Hals tad Press.
4. Fowlkes, E. B., Gnanadesikan, R., Kettenring, J. R. 1988. Vari- .able Selection in Clustering. Journal of Classification 5: 205. 228.
5. Gath, I., Geva, A. B. 1989. Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(7): 773-781.
6. Gnanadesikan, R., Kettenring, J. R., Tsao, S. L. 1992. Weighting and Selection of Variables. Journal of Classification 12: 113136.
7. Green, P. E., Cannone, F. J., Kim, J. 1990. A Preliminary Study of Optimal Variable Weighting in K-Means Clustering. Journal of Classification 7: 271-285.
8. Hammah, R. E. 1998. Intelligent Delineation of Rock Discontinuity Data using Fussy Cluster Analysis. Ph.D. Thesis: University of Toronto.
9. Hammah, R. E., Curran, J. H. 1998. Fuzzy cluster algorithm for the automatic delineation of joint sets. Int. Jour. Rock Mech. & Min. Sci. 35(7): 889-905.
10. Hammah, R. E., Curran, J. H. 1999b. On distance measures for the fuzzy K-means algorithm for joint data. Rock Mechanics and Rock Engineering 32(1): 1-27.
11. Hohenegger, J. 1986. Weighted Standardization - A General Data Transformation Method Preceeding Classification Procedures. Biometrkal Journal 3: 295-303.
12. Jain, A. K. 1986. Cluster Analysis. In, Handbook of Pattern Recognition and Image Processing: 33-57. Orlando: Academic Press.
13. Jain, A. K., Dubes, R. C. 1988. Algorithms for Clustering Data, Englewood Cliffs. Prentice Hall.
14. Lumelsky, V. 1982. A Combined Algorithm for Weighting the Variables and Clustering in the Clustering Problem Pattern Recognition 15(6): 53-60.
15. Milligan, G. W. 1989. A validation study of a variable weighting algorithm for cluster analysis. Journal of Classification 6: 5371.
16. Milligan, G. W. 1996. Clustering validation: results and implications for applied analyses. In P. Arabie, I. J. Hubert and G. De Sode (eds), Clustering and Classification: 341-375. River Edge: World Scientific Publications, .
17. Milligan, G. W., Cooper, M. C. 1988. A Study of Standardization of Variables in Cluster Analysis. Journal of Classification 5: 181-204.
18. Morrison, D. G. 1967. Measurement Problems in Cluster Analysis. Management Science 13: 775-780.