Cooperative clustering

Pattern Recognition

Volumn 43, Issue 6, 2010, Pages 2315-2329

  Kashef, Rasha ^a   Kamel, Mohamed S ^b  

^a Egypt Mansoura University   (Egypt)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Cooperative clustering; Cooperative contingency graph; Similarity histogram

Indexed keywords

CLUSTERING APPROACH; CLUSTERING MODEL; CLUSTERINGS; COOPERATIVE MODEL; DATA CLUSTERING; DATA SETS; GRAPH SIMILARITY; MACHINE-LEARNING; MERGING PROCESS; MULTIPLE CLUSTERINGS; OPTIMUM SOLUTION; SUB-CLUSTERS; TEXT DOCUMENT;

BIOINFORMATICS; CLUSTER ANALYSIS; DATA STRUCTURES; GENE EXPRESSION; PATTERN RECOGNITION;

CLUSTERING ALGORITHMS;

EID: 76749131516     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2009.12.018     Document Type: Article

References (41)

1. Jain, Murty M., and Flynn P. Data clustering: a review. ACM Computing Surveys 31 (1999) 264-323
2. Xu R. Survey of clustering algorithms. IEEE Transactions on Neural Networks 16 3 (2005) 645-678
3. M. Steinbach, G. Karypis, V. Kumar, A Comparison of document clustering techniques, in: Proceedings of the KDD Workshop on Text Mining, 2000, pp. 109-110.
4. Duda R., and Hart P. Pattern Classification and Scene Analysis (1973), Wiley
5. Hartigan J., and Wong M. A k-means clustering algorithm. Applied Statistics 28 (1979) 100-108
6. Bezdek J., Ehrlich R., and Full W. The fuzzy C-means clustering algorithm. Computers and Geosciences 10 (1984) 191-203
7. S.M. Savaresi, D. Boley, On the performance of bisecting K-means and PDDP, in: Proceedings of the First SIAM International Conference on Data Mining, 2001, pp. 1-14.
8. Boley D. Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2 4 (1998) 325-344
9. K. Hammouda, M. Kamel, Collaborative document clustering, 2006 SIAM Conference on Data Mining (SDM06), 2006, pp. 453-463.
10. Kashef R., and Kamel M. Enhanced bisecting k-means clustering using intermediate cooperation. Pattern Recognition 42 11 (2009) 2557-2569
11. Y. Zhao, G. Karypis, Criterion functions for document clustering: experiments and analysis, Technical Report, 2002.
12. R. Kashef, M.S. Kamel, Cooperative partitional-divisive clustering and its application in gene expression analysis, in: IEEE Seventh International Conference on BioInformatics and BioEngineering (BIBE07), 2007, pp. 116-122.
13. Ning H., Xu W., Chi Y., Gong Y., and Huang T. Incremental spectral clustering by efficiently updating the eigen-system. Pattern Recognition 43 1 (2010) 113-127
14. Fan J., Han M., and Wang J. Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation. Pattern Recognition 42 11 (2009) 2527-2540
15. Lai J., Huang T., and Liaw Y. A fast k-means clustering algorithm using cluster center displacement. Pattern Recognition 42 11 (2009) 2551-2556
16. Qin A., and Suganthan P. Robust growing neural gas algorithm with application in cluster analysis. Neural Networks 17 8-9 (2004) 1135-1148
17. Kim J., and Choi S. Clustering with r-regular graphs. Pattern Recognition 42 9 (2009) 2020-2028
18. Qin A., and Suganthan P. Enhanced neural gas network for prototype-based clustering. Pattern Recognition 38 8 (2005) 1275-1288
19. A. Strehl, J. Ghosh, Cluster ensembles-knowledge reuse framework for combining partitionings, in: Conference on Artificial Intelligence, AAAI/MIT Press, 2002, pp. 93-98.
20. Y. Qian, C. Suen, Clustering combination method, in: International Conference on Pattern Recognition, ICPR 2000, vol. 2, 2000, pp. 732-735.
21. D. Greene, P. Cunningham, Efficient ensemble methods for document clustering, Technical Report, Computer Science Department, Trinity College Dublin, 2006.
22. Ayad H., and Kamel M. Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters, IEEE Transactions on Pattern Analysis and Machine Intelligence (2007), IEEE Computer Society Digital Library, IEEE Computer Society
23. Lin C., and Chen M. Combining partitional and hierarchical algorithms for robust and efficient data clustering with cohesion self-merging. IEEE Transactions on Knowledge and Data Engineering 17 2 (2005) 145-159
24. Y. Eng, C. Kwoh, Z. Zhou, On the two-level hybrid clustering algorithm, AISAT04, in: International Conference on Artificial Intelligence in Science and Technology, 2004, pp. 138-142
25. Xu S., and Zhang J. A hybrid parallel web document clustering algorithm and its performance study. Journal of Supercomputing 30 2 (2004) 117-131
26. Ismail M., and Kamel M. Multidimensional data clustering utilizing hybrid search strategies. Pattern Recognition 22 (1989) 75-89
27. Kaufmann L., and Rousseeuw P. Finding Groups in Data (1990), Wiley, New York
28. R. Ng, J. Han, Efficient and effective clustering methods for spatial data mining, in: VLDB, 1994, pp. 144-155.
29. Salton G. Automatic Text Processing: The Transformation Analysis and Retrieval of Information by Computer (1989), Reading Mass: Addison Wesley
30. Maulik U., and Bandyopadhyay S. Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24 12 (2002) 1650-1654
31. Wu K., Yang M., and Hsieh J. Robust cluster validity indexes. Pattern Recognition 42 11 (2009) 2541-2550
32. Monti S., Tamayo P., Mesirov J., and Golub T. Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data (2003), Kluwer Academic Publishers
33. Tavazoie S., Hughes J., Campbell M., Cho R., and Church G. Systematic determination of genetic network architecture. Nature Genetics 22 (1999) 281-285
34. West M., Blanchette C., Dressman H., Huang E., Ishida S., Spang R., Zuzan H., Olson J., Marks J., and Nevins J. Predicting the clinical status of human breast cancer by using gene expression profiles. Proceedings of the National Academy of Sciences, USA 98 (2001) 11462-11467
35. 〈http://www.sciencemag.org/feature/data/984559.sh〉
36. Golub T., Slonim D., Tamayo P., Huard C., Gaasenbeek M., Mesirov J., Coller H., Loh M., Downing J., Caligiuri M.A., Bloomfield C., and Lander E. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 5439 (1999) 531-537
37. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S., and Golub T. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. PNAS 96 (1999) 2907-2912
38. Eisen M., Spellman P., Brown P., and Botstein D. Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America 95 25 (1998) 14863-14868
39. Salton G., Wong A., and Yang C. A vector space model for automatic indexing. Communications of the ACM 18 11 (1975) 613-620
40. Cai D., He X., and Han J. Document clustering using locality preserving indexing. IEEE Transactions on Knowledge and Data Engineering 17 12 (2005) 1624-1637
41. Boley D., Gini M., Gross R., Han S., Hastings K., Karypis G., Kumar V., Mobasher B., and Moore J. Partitioning-based clustering for web document categorization. Decision Support Systems 27 (1999) 329-341