A new shifting grid clustering algorithm

Pattern Recognition

Volumn 37, Issue 3, 2004, Pages 503-514

  Ma, Eden W M ^a   Chow, Tommy W S ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Clustering; Shifting grid

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS;

GRID STRUCTURES;

PATTERN RECOGNITION;

ALGORITHM;

EID: 0346216908     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2003.08.014     Document Type: Article

References (26)

1. Han J. Kamber M. Data Mining Concepts and Techniques 2001 Morgan Kaufmann Publisher San Franciso
2. Jain A.K. Duin R.P.W. Mao J. Statistical pattern recognition: a review IEEE Trans. Pattern Anal. Mach. Intell. 22 1 2000 4-37
3. Everitt B. Landau S. Leese M. Cluster Analysis 4th Edition 2001 Arnold London
4. Jain A.K. Murty M.N. Flynn P.J. Data Clustering: a review ACM Comput. Surveys 31 3 1999 264-323
5. P. Berkhin, Survey of Clustering Data Mining Techniques, http://www.accrue.com/products/rp_cluster_review.ps
6. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large data bases, Proceedings of the ACM SIGMOD International Conference on Management of Data, Vol. 25, No. 2 1996, pp. 103-114.
7. S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large data bases, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Vol. 27, No. 2, 1998, pp. 73-84.
8. Karypis G. Han E.H. Kumar V. CHAMELEON: a hierarchical clustering algorithm using dynamic modeling IEEE Comput. 32 8 1999 68-75
9. S. Guha, R. Rastogi, K. Shim, ROCK: a robust clustering algorithm for categorical attributes, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 512-521.
10. J.B. MacQueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability, 1967, pp. 281-297.
11. J.C. Dunn, A fuzzy relative of the ISODATA process and its use detecting compact well-separated clusters, Journal of Cybernetics 3 (1974) 32-57.
12. Z. Huang, A fast clustering algorithm to cluster very large categorical data sets in data mining, in: SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997.
13. Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values Data Mining Knowledge Discovery 2 1998 283-304
14. Wong C.C. Chen C.C. Su M.C. A novel algorithm for data clustering Pattern Recognition 34 2001 425-442
15. Vrahatics M.N. Boutsinas B. Alevizos P. Pavlides G. The new k-windows algorithm for improving the k-Means clustering algorithm J. of Complexity 18 2002 375-391
16. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial data bases with noise, in: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996.
17. A. Hinneburg, D.A. Keim, An Efficient Approach to Clustering in Large Multimedia Data bases with Noise, American Association for Artificial Intelligence, Proceedings of the 1998 International Conference on Knowledge Discovery and Data Mining (KDD'98), pp. 58-65.
18. M. Ankerst, M.M. Breunig, H.-P. Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: Proceeding of International Conference on Management of Data, 1999, pp. 49-60.
19. W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, Proceedings of the International Conference on Very Large Data Bases, 1997, pp. 186-195.
20. W. Wang, J. Yang, R. Muntz, STING+: an approach to active spatial data mining, Proceedings of 15th International Conference on Data Engineering, 1999, pp. 116-125.
21. Sheikholeslami G. Chatterjee S. Zhang A. WaveCluster: a wavelet-based clustering approach for spatial data in very large data bases VLDB J. 8 2000 289-304
22. R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the ACM SIGMOD International Conference on Management of data, 1998, pp. 94-105.
23. Y.C. Zhao, J. Song, GDILC: a grid-based density-isoline clustering algorithm, Proceedings of International Conferences on Info-tech and Info-net, Vol. 3, 2001, pp. 140-145.
24. http://www.ics.uci.edu/~mlearn/MLRepository.html
25. E. Schikuta, Grid-clustering: an efficient hierarchical clustering method for very large data sets, Proceedings of ICPR, 1996, pp. 101-105.
26. Yin H. Data visualisation and manifold mappling using the ViSOM Neural Networks 15 2002 1005-1016