Adaptive K-means clustering to handle heterogeneous data using basic rough set theory

Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

Volumn 84, Issue PART 1, 2012, Pages 193-202

  Tripathy, B K ^a   Ghosh, Adhir ^a   Panda, G K ^b  

^a VIT UNIVERSITY   (India)

^b MITS Engineering College   (India)

Author keywords

Classification; Cluster; Crisp boundaries; Heterogeneous data; Uncertainty

Indexed keywords

CLUSTER; CLUSTER ANALYSIS TECHNIQUE; HETEROGENEOUS DATA; INCOMPLETE INFORMATION; K-MEANS ALGORITHM; K-MEANS CLUSTERING; STATISTICAL CLUSTERING TECHNIQUE; UNCERTAINTY;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; COMPUTER SCIENCE; INFORMATION TECHNOLOGY; KNOWLEDGE REPRESENTATION;

ROUGH SET THEORY;

EID: 84888414617     PISSN: 18678211     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-27299-8_21     Document Type: Conference Paper

References (19)

1. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39(1), 1-38 (1977)
2. Ganti, V., Gehrke, J., Ramakrishnan, R.: CACTUS - clustering categorical data using summaries. In: Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 73-83 (1999)
3. Gibson, D., Kleinberg, J., Raghavan, P.: Clustering categorical data: an approach based on dynamical systems. The Very Large Data Bases Journal 8(3-4), 222-236 (2000)
4. Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. Information Systems 25(5), 345-366 (2000)
5. Haimov, S., Michalev, M., Savchenko, A., Yordanov, O.: Classification of radar signatures by autoregressive model fitting and cluster analysis. IEEE Transactions on Geo Science and Remote Sensing 8(1), 606-610 (1989)
6. Han, E., Karypis, G., Kumar, V., Mobasher, B.: Clustering based on association rule hypergraphs. In: Workshop on Research Issues on Data Mining and Knowledge Discovery, pp. 9-13 (1997)
7. Hartigan, J.A., Wong, M.A.: Algorithm AS136: A K-Means Clustering Algorithm. Applied Statistics 28, 100-108 (1979)
8. Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical value. Data Mining and Knowledge Discovery 2(3), 283-304 (1998)
9. Jiang, D., Tang, C., Zhang, A.: Cluster analysis for gene expression data: a survey. IEEE Transactions on Knowledge and Data Engineering 16(11), 1370-1386 (2004)
10. Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Transactions on Fuzzy Systems 1(2), 98-110 (1993)
11. Krishnapuram, R., Frigui, H., Nasraoui, O.: Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. IEEE Transactions on Fuzzy Systems 3(1), 29-60 (1995)
12. MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297. University of California Press (1967)
13. Mathieu, R., Gibson, J.: A Methodology for large scale R&D planning based on cluster analysis. IEEE Transactions on Engineering Management 40(3), 283-292 (2004)
14. Parmar, D., Teresa, W., Jennifer, B.: MMR: An algorithm for clustering categorical data using Rough Set Theory. Data & Knowledge Engineering, 879-893 (2007)
15. Pawlak, Z.: Rough Sets- Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell (1992)
16. Tripathy, B.K., Kumar, P.: MMeR: An algorithm for clustering Heterogeneous data using rough Set Theory. International Journal of Rapid Manufacturing (special issue on Data Mining) 1(2), 189-207 (2009)
17. Wong, K., Feng, D., Meikle, S., Fulham, M.: Segmentation of dynamic pet images using cluster analysis. IEEE Transactions on Nuclear Science 49(1), 200-207 (2002)
18. Wu, S., Liew, A., Yang, M.: Cluster analysis of gene expression data based on selfsplitting and merging competitive learning. IEEE Transactions on Information Technology in Bio Medicine 8(1), 5-15 (2004)
19. Zadeh, L.A.: Fuzzy Sets. Information and Control 11, 338-353 (1965)