An empirical study on the visual cluster validation method with Fastmap

Proceedings - 7th International Conference on Database Systems for Advanced Applications, DASFAA 2001

Volumn , Issue , 2001, Pages 84-91

  Huang, Z ^a   Cheung, D W ^a   Ng, M K ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; DATA HANDLING; DATA MINING; DATABASE SYSTEMS;

2-D SPACE; CLUSTER VALIDATION; CLUSTERING PROBLEMS; DATA MINING APPLICATIONS; DATA PARTITION; EMPIRICAL STUDIES; STATISTICAL VALIDATION; VALIDATION RESULTS;

MONTE CARLO METHODS;

EID: 84963830496     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DASFAA.2001.916368     Document Type: Conference Paper

References (24)

1. Agrawal, R., Gehrke, J, Gunopulos, D. and Raghavan, P. (1998) Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of SIGMOD Conference.
2. Cormack, R. (1971) A review of classification. Journal of Royal Statistical Society, Series A, Vol. 134, pp. 321-367.
3. Dubes, R. C. (1987) How many clusters are best? - an experiment. Pattern Recognition, Vol. 20, No. 6, pp.645-663.
4. Dubes, R. and Jain, A. K. (1979) Validity studies in clustering methodologies. Pattern Recognition, Vol. 11, pp. 235-254.
5. Ester, M., Kriegel, H.-P., Sander, J. and Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery in Databases and Data Mining, Portland, Oregon, USA.
6. Everitt, B. (1974) Cluster Analysis. Heinemann Educational Books Ltd.
7. Faloutsos, C. and Lin, K., (1995) Fastmap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets. In Proceedings of ACM-SIGMOD, pp. 163-174.
8. Fukunaga, K. (1990) Introduction to Statistical Pattern Recognition. Academic Press.
9. Ganti, V., Ramakrishnan, R., Gehrke, J, Powell, A. L. and French, J. C. (1999) Clustering large datasets in arbitrary metric spaces. ICDE 1999, pp. 502-511.
10. Gordon, A. D. (1998) Cluster validation, In Data Science, Classification, and Related Methods, ed. C Hayashi, N Ohsumi, K Yajima, Y Tanaka, H-H Bock and Y Baba, Springer, Tokyo, pp 22-39.
11. Gordon, A. D. (1994) Identifying genuine clusters in a classification. Computational Statistics and Data Analysis 18, pp.516-581.
12. Huang, Z. (1998) Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, Vol. 2, No. 3, pp. 283-304.
13. Huang, Z. and Lin, T. (2000) A visual method of cluster validation with Fastmap. In Proceedings of PAKDD-2000, pp. 153-164.
14. Jain, A. K. and Dubes, R. C. (1988) Algorithms for Clustering Data. Prentice Hall.
15. Kruskal, J. B. and Carroll, J. D. (1969) Geometrical models and badness-of-fit functions, in Multivariate Analysis II, ed. P. R. Krishnaiah, Academic Press, pp.639-670.
16. Milligan, G. W. (1996) Clustering validation: results and implications for applied analysis. in Clustering and Classification, ed. P. Arabie, L. J. Hubert and G. De Soete, World Scientific, pp.341-375.
17. Milligan, G. W. (1981) A Monte Carlo study of thirty internal criterion measures for cluster analysis. Psychometrika, Vol. 46, No. 2, pp.187-199.
18. Milligan, G. W. and Cooper, M. C. (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika, Vol. 50, No. 2, pp.159-179.
19. Milligan, G. W. and Isaac, P. D. (1980) The validation of four ultrametric clustering algorithms. Pattern Recognition, Vol. 12, pp.41-50.
20. Ng, R. and Han, J. (1994) Efficient and effective clustering methods for spatial data mining. In Proceedings of VLDB, 1994.
21. Rousseeuw, P. J. (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, Vol. 20, pp.53-65.
22. Theodoridis, S. and Koutroumbas, K. (1999) Pattern Recognition. Academic Press.
23. Young, F. W. (1987) Multidimensional scaling: history, theory and applications. Lawrence Erlbaum Associates.
24. Zhang, T. and Ramakrishnan, R. (1997) BIRCH: A new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, Vol. 1, No. 2, pp. 141-182.