MMR: An algorithm for clustering categorical data using Rough Set Theory

Data and Knowledge Engineering

Volumn 63, Issue 3, 2007, Pages 879-893

  Parmar, Darshit ^a   Wu, Teresa ^a   Blackhurst, Jennifer ^a  

^a Arizona State University   (United States)

Author keywords

Categorical data; Cluster analysis; Data mining; Rough Set Theory

Indexed keywords

CLUSTERING ALGORITHMS; ROUGH SET THEORY; SYSTEM STABILITY; UNCERTAINTY ANALYSIS;

CATEGORICAL DATA; CLUSTER ANALYSIS TECHNIQUES; MIN-MIN-ROUGHNESS (MMR);

DATA MINING;

EID: 34548666399     PISSN: 0169023X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.datak.2007.05.005     Document Type: Article

References (42)

1. P. Andritsos, P. Tsaparas, R.J. Miller, K.C. Sevcik, Clustering categorical data based on information loss minimization, in: Second Hellenic Data Management Symposium, 2003, pp. 334-344.
2. Dempster A., Laird N., and Rubin D. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39 1 (1977) 1-38
3. Dimitras A.I., Slowinski R., Susmaga R., and Zopounidis C. Business failure prediction using rough sets. European Journal of Operational Research 114 22 (1999) 263-280
4. V., Ganti, J. Gehrke, R. Ramakrishnan, CACTUS - clustering categorical data using summaries, in: Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999, pp. 73-83.
5. Gibson D., Kleinberg J., and Raghavan P. Clustering categorical data: an approach based on dynamical systems. The Very Large Data Bases Journal 8 3-4 (2000) 222-236
6. Guha S., Rastogi R., and Shim K. ROCK: a robust clustering algorithm for categorical attributes. Information Systems 25 5 (2000) 345-366
7. Halkidi M., Batistakis Y., and Vazirgiannis M. On clustering validation techniques. Journal of Intelligent Information Systems 17 2-3 (2001) 107-145
8. Haimov S., Michalev M., Savchenko A., and Yordanov O. Classification of radar signatures by autoregressive model fitting and cluster analysis. IEEE Transactions on Geo Science and Remote Sensing 8 1 (1989) 606-610
9. E. Han, G. Karypis, V. Kumar, B. Mobasher, Clustering based on association rule hypergraphs, in: Workshop on Research Issues on Data Mining and Knowledge Discovery, 1997, pp. 9-13.
10. Z. He, X. Xu, S. Deng, A link clustering based approach for clustering categorical data, Proceedings of the WAIM Conference, 2004. .
11. He Z., Xu X., and Deng S. Squeezer: an efficient algorithm for clustering categorical data. Journal of Computer Science & Technology 17 5 (2002) 611-624
12. Huang Z. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery 2 3 (1998) 283-304
13. Jiang D., Tang C., and Zhang A. Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16 11 (2004) 1370-1386
14. Johnson R., and Wichern W. Applied Multivariate Statistical Analysis (2002), Prentice Hall, New York
15. A. Joshi, R. Krishnapuram, Robust fuzzy clustering methods to support web mining, in: Proceedings of the Workshop on Data Mining and Knowledge Discovery, vol. 15, 1998, pp. 1-8.
16. Kim D., Lee K., and Lee D. Fuzzy clustering of categorical data using fuzzy centroids. Pattern Recognition Letters 25 11 (2004) 1263-1271
17. Krishnapuram R., Frigui H., and Nasraoui O. Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. IEEE Transactions on Fuzzy Systems 3 1 (1995) 29-60
18. Krishnapuram R., and Keller J. A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1 2 (1993) 98-110
19. Kowalczyk W., and Slisser F. Analyzing customer retention with rough data models. Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery (1997), Springer-Verlag, Trondheim, Norway pp. 4-13
20. Kusiak A. Rough set theory: a data mining tool for semiconductor manufacturing. IEEE Transactions on Electronics Packaging Manufacturing 24 1 (2001) 44-50
21. Lee S., and Vachtsevanos G. An application of rough set theory to defect detection of automotive glass. Mathematics and Computers in Simulation 60 3-5 (2002) 225-231
22. Lingras P. Unsupervised rough set classification using GAs. Journal of Intelligent Information Systems 16 3 (2001) 215-228
23. Lingras P., Hogo M., and Snorek M. Interval set clustering of web users using modified kohonen self-organizing maps based on the properties of rough sets. Web Intelligence and Agent Systems 2 3 (2004) 217-225
24. Lingras P., and West C. Interval set clustering of web users with rough K-means. Journal of Intelligent Information Systems 23 1 (2004) 5-16
25. Lingras P., Yan P.R., and Hogo M. Rough set based clustering: evolutionary, neural, and statistical approaches. Proceedings of the First Indian International Conference on Artificial Intelligence (2003) 1074-1087
26. Mathieu R., and Gibson J. A Methodology for large scale R&D planning based on cluster analysis. IEEE Transactions on Engineering Management 40 3 (2004) 283-292
27. L. Mazlack, A. He, Y. Zhu, S. Coppock, A rough set approach in choosing partitioning attributes, in: Proceedings of the ISCA 13th International Conference (CAINE-2000), 2000, pp. 1-6.
28. Nguyen S.H., and Nguyen H.S. Pattern extraction from data. Fundamentra Informaticae 34 1-2 (1998) 1-16
29. Pawlak Z. Rough sets. International Journal of Computer and Information Sciences 11 5 (1982) 341-356
30. Pawlak Z. Rough Sets - Theoretical Aspects of Reasoning about Data (1991), Kluwer Academic Publishers, Boston
31. Pawlak Z. Rough set approach to knowledge-based decision support. European Journal of Operational Research 99 1 (1997) 48-57
32. Pawlak Z. Rough classification. International Journal of Man-Machine Studies 20 5 (1984) 469-483
33. Ralambondrainy H. A conceptual version of the K-means algorithm. Pattern Recognition Letters 16 11 (1995) 1147-1157
34. Ruspini E. A new approach to clustering. Information Control 15 1 (1969) 22-32
35. Tseng T.-L., Jothishankar M.C., and Wu T. Quality control problem in printed circuit manufacturing - an extended rough set approach. Journal of Manufacturing Systems 23 1 (2004) 56-72
36. S. Tsumoto, Extraction of experts' decision process from clinical databases using rough set model, in: Proceedings of the First European Symposium, 1997, pp. 58-67.
37. Voges K., Pope N., and Brown M. Cluster analysis of marketing data examining on-line shopping orientation: a comparison of K-means and rough clustering approaches. In: A Abbass H., A Sarker R., and Newton C.S. (Eds). Heuristics and Optimization for Knowledge Discovery (2002), Idea Group Publishing, Hershey, PA 207-224
38. Voges K., and Pope N. Generating compact rough cluster descriptions using an evolutionary algorithm. In: Deb K. (Ed). GECCO2004: Genetic and Evolutionary Algorithm Conference - LNCS (2004), Springer-Verlag, Berlin 1332-1333
39. Wong K., Feng D., Meikle S., and Fulham M. Segmentation of dynamic pet images using cluster analysis. IEEE Transactions on Nuclear Science 49 1 (2002) 200-207
40. Wu S., Liew A., Yan H., and Yang M. Cluster analysis of gene expression data based on self-splitting and merging competitive learning. IEEE Transactions on Information Technology in BioMedicine 8 1 (2004) 5-15
41. Y. Zhang, A. Fu, C. Cai, P. Heng, Clustering categorical data, in: Proceedings of the 16th International Conference on Data Engineering, 2000, pp. 305-324.
42. W. Ziarko, Rough sets for intelligent image processing, in: Proceedings of the International Workshop on Rough Sets and Knowledge Discovery, 1993, pp. 399-410.