Parallel Fuzzy c-means cluster analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4395 LNCS, Issue , 2007, Pages 52-65

  Modenesi, Marta V ^a   Costa, Myrian C A ^a   Evsukoff, Alexandre G ^a   Ebecken, Nelson F F ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

Author keywords

Cluster and grid computing; Fuzzy c means; Unsupervised classification

Indexed keywords

CLUSTER ANALYSIS; COMPUTER AIDED SOFTWARE ENGINEERING; DATABASE SYSTEMS; GRID COMPUTING; PARALLEL PROCESSING SYSTEMS;

BUSINESS DATABASES; PROCESSING TIME; UNSUPERVISED CLASSIFICATION;

FUZZY SYSTEMS;

EID: 38049165601     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-71351-7_5     Document Type: Conference Paper

References (20)

1. M. S. R. Sousa, M. Mattoso and N. F.F. Ebecken (1999). Mining a large database with a parallel database server. Intelligent Data Analysis 3, pp. 437-451.
2. M. Coppola, and M. Vanneschi. (2002). High-performance data mining with skeleton-based structured parallel programming. Parallel Computing 28, pp. 783-813.
3. R. Jin, G. Yang, and G. Agrawal (2005). Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance. IEEE Transaction on Knowledge and Data Engineering, vol. 17, no. 1, pp. 71-89.
4. M. Cannataro, A. Congiusta, A. Pugliese, D. Talia, P. Trunfio (2004). Distributed data mining on grids: services, tools, and applications. IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 34, no. 6, pp. 2451-2465.
5. K. Kubota, A. Nakase, H. Sakai and S. Oyanagi (2000). Parallelization of decision tree algorithm and its performance evaluation. Proceedings of the Fourth International Conference on High Performance Computing in the Asia-Pacific Region, vol. 2, pp. 574-579.
6. M. W. Kim, J. G. Lee and C. Min (1999). Efficient fuzzy rule generation based on fuzzy decision tree for data mining. Proceedings of the IEEE International Fuzzy Systems Conference FUZZ-IEEE '99. pp1223-1228.
7. A. Evsukoff, M. C. A. Costa and N. F. F. Ebecken (2004). Parallel Implementation of Fuzzy Rule Based Classifier. Proceedings of the VECPAR'2004, vol. 2, pp. 443-452.
8. P. K. H. Phua and D. Ming. (2003). Parallel nonlinear optimization techniques for training neural networks. IEEE Transactions on Neural Networks, vol. 14, no. 6, pp. 1460-1468.
9. M. C. A. Costa and N. F. F. Ebecken (2001). A Neural Network Implementation for Data Mining High Performance Computing. Proceedings of the V Brazilian Conference on Neural Networks, pp. 139-142.
10. R. Agrawal and J. C. Shafer (1996).Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962-969.
11. L. Shen, H. Shen and L. Cheng (1999). New algorithms for effcient mining of association rules. Information Sciences 118, pp. 251-268.
12. B. Boutsinas and T. Gnardellis (2002). On distributing the clustering process. Pattern Recognition Letters 23, pp. 999-1008.
13. S. Rahimi, M. Zargham, A. Thakre and D. Chhillar (2004) A parallel Fuzzy C-Mean algorithm for image segmentation. Proceedings of the IEEE Annual Meeting of the Fuzzy Information NAFIPS '04, vol. 1, pp. 234-237.
14. A. K. Jain, M. N. Murty and P. J. Flynn (1999). Data clustering: a review. ACM Computing Surveys, vol. 31, no. 3. pp. 264-323.
15. J. C. Bezdek (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. New York, Plenum.
16. X. L. Xie and G. A. Beni (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 3 no. 8, pp. 841-846.
17. J. Bezdek and N.R. Pal (1998). Some new indexes of cluster validity. IEEE Trans. Systems Man and Cybernetics B, vol. 28, pp. 301-315.
18. M. K. Pakhira, S. Bandyopadhyay and U. Maulik (2004). Validity index for crisp and fuzzy clusters. Pattern Recognition, vol. 37, pp. 487-501.
19. I. H. Witten and E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Morgan Kaufmann, San Francisco.
20. R. Quinlan (1993). C4.5 - Programs for Machine Learning. Morgan Kaufmann, San Francisco.