Multiobjective genetic clustering with ensemble among pareto front solutions: Application to MRI brain image segmentation

Proceedings of the 7th International Conference on Advances in Pattern Recognition, ICAPR 2009

Volumn , Issue , 2009, Pages 236-239

  Mukhopadhyay, Anirban ^a   Maulik, Ujjwal ^b   Bandyopadhyay, Sanghamitra ^c  

^a UNIVERSITY OF KALYANI   (India)

^b JADAVPUR UNIVERSITY   (India)

^c INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Cluster validity measures; Clustering ensemble; Multiobjective fuzzy clustering; Pareto optimality

Indexed keywords

CLUSTERING ALGORITHMS; FUZZY SYSTEMS; IMAGE SEGMENTATION; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; PATTERN RECOGNITION;

BRAIN IMAGES; CLUSTER CENTERS; CLUSTER VALIDITY MEASURES; CLUSTERING ENSEMBLE; CLUSTERING SCHEMES; EXPECTATION MAXI- MIZATION; FUZZY C-MEANS; FUZZY CLUSTERS; GENETIC-FUZZY; K-MEANS; MULTI-OBJECTIVE GENETIC ALGORITHMS; MULTIOBJECTIVE CLUSTERING; MULTIOBJECTIVE FUZZY CLUSTERING; NON-DOMINATED SOLUTIONS; NSGA-II; OPTIMAL CLUSTERING; PARETO FRONTS; PARETO OPTIMALITY; PROTON DENSITIES; REAL-CODED; SEARCH CAPABILITIES; SINGLE OBJECTIVES;

FUZZY CLUSTERING;

EID: 63649095026     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAPR.2009.51     Document Type: Conference Paper

References (17)

1. A. K. Jain and R. C. Dubes, "Data clustering: A review," ACM Computing Surveys, vol. 31, 1999.
2. U. Maulik and S. Bandyopadhyay, "Performance evaluation of some clustering algorithms and validity indices," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1650-1654, 2002.
3. -, "Genetic algorithm based clustering technique," Pattern Recognition, vol. 33, pp. 1455-1465, 2000.
4. -, "Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 5, pp. 1075-1081, 2003.
5. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms. England: John Wiley and Sons, Ltd, 2001.
6. C. A. Coello Coello, "Evolutionary multiobjective optimization: A historical view of the field," IEEE Computational Intelligence Magazine, vol. 1, no. 1, pp. 28-36, 2006.
7. S. Bandyopadhyay, U. Maulik, and A. Mukhopadhyay, "Multiobjective genetic clustering for pixel classification in remote sensing imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1506-1511, 2007.
8. S. Bandyopadhyay, S. Saha, U. Maulik, and K. Deb, "A simulated annealing-based multiobjective optimization algorithm: AMOSA," IEEE Transactions on Evolutionary Computation, vol. 12, no. 3, pp. 269-283, 2008.
9. K. Deb, S. Agrawal, A. Pratab, and T. Meyarivan, "A fast elitist non-dominated sorting genetic algorithm for multiobjective optimization: NSGA-II," in Proc. Parallel Problem Solving from Nature VI Conference. Paris, France: Springer. Lecture Notes in Computer Science No. 1917, 2000, pp. 849-858.
10. X. L. Xie and G. Beni, "A validity measure for fuzzy clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, pp. 841-847, 1991.
11. M. Pakhira, S. Bandyopadhyay, and U. Maulik, "Validity index for crisp and fuzzy clusters," Pattern Recognition, vol. 37, pp. 487-501, 2004.
12. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
13. A. Strehl and J. Ghosh, "Cluster ensembles - a knowledge reuse framework for combining multiple partitions," J. Machine Learning Research, vol. 3, pp. 583-617, 2002.
14. E. Zitzler and L. Thiele, "An evolutionary algorithm for multiobjective optimization: The Strength Pareto approach," Gloriastrasse 35, CH-8092 Zurich, Switzerland, Tech. Rep. 43, 1998.
15. E. Zitzler, M. Laumanns, and L. Thiele, "SPEA2: Improving the Strength Pareto Evolutionary Algorithm," Gloriastrasse 35, CH-8092 Zurich, Switzerland, Tech. Rep. 103, 2001.
16. "Brainweb: Simulated brain database," Available at: http://www.bic.mni.mcgill.ca/brainweb.
17. K. Y. Yeung and W. L. Ruzzo, "An empirical study on principal component analysis for clustering gene expression data," Bioinformatics, vol. 17, no. 9, pp. 763-774, 2001.