Data clustering using variants of rapid centroid estimation

IEEE Transactions on Evolutionary Computation

Volumn 18, Issue 3, 2014, Pages 366-377

  Yuwono, Mitchell ^a   Su, Steven W ^a   Moulton, Bruce D ^a   Nguyen, Hung T ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

algorithm design and analysis; clustering algorithms; computational complexity; particle swarm optimization

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; ITERATIVE METHODS; PARTICLE SWARM OPTIMIZATION (PSO);

ALGORITHM DESIGN AND ANALYSIS; BENCHMARK DATASETS; BENCHMARK EVALUATION; CENTROID ESTIMATION; CLUSTERING PROBLEMS; OPTIMIZATION STRATEGY; PARTICLE SWARM CLUSTERING; PARTICLE TRAJECTORIES;

CLUSTERING ALGORITHMS;

EID: 84901855861     PISSN: 1089778X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TEVC.2013.2281545     Document Type: Article

References (29)

1. C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image segmentation using expectation-maximization and its application to image querying," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 8, pp. 1026-1038, Aug. 2002.
2. L. Jing, M. Ng, and J. Huang, "An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data," IEEE Trans. Knowledge Data Eng., vol. 19, no. 8, pp. 1026-1041, Aug. 2007. (Pubitemid 47000341)
3. M. Yuwono, B. D. Moulton, S. W. Su, B. G. Celler, and H. T. Nguyen, "Unsupervised machine learning method for improving the performance of ambulatory fall detection systems," BioMed. Eng. OnLine, vol. 11, no. 9, pp. 1-11, 2012. [Online]. Available: http://www.biomedicalengineering- online.com/content/11/1/9
4. M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen, "Gait episode identification based on wavelet feature clustering of spectrogram images," in Proc. 34rd Annu. Int. Conf. IEEE EMBS, Aug.-Sep. 2012, pp. 2949-2952.
5. M. Yuwono, J. Sepulveda, and A. M. A. Handojoseno, "Centroid extraction from Hartmann-Shack images using swarm clustering approach," in Proc. 34rd Annu. Int. Conf. IEEE EMBS, Aug.-Sep. 2012, pp. 1446-1449.
6. D. W. van der Merwe and A. P. Engelbrecht, "Data clustering using particle swarm optimization," in Proc. IEEE CEC, vol. 1. Dec. 2003, pp. 215-220.
7. A. Abraham, S. Das, and S. Roy, "Swarm intelligence algorithms for data clustering," in Soft Computing for Knowledge Discovery and Data Mining, O. Maimon and L. Rokach, Eds. New York, USA: Springer, 2008, pp. 279-313.
8. W. C. Chiang, G. Mudunuri, G. Cai, W. Zhu, and X. Xu, "Two-stage tabu: Particle swarm algorithms for the facility layout problem with size constraints," in Proc. IEEE CEC, Jun. 2011, pp. 1679-1686.
9. R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proc. 6th Int. Symp. Micro Mach. Human Sci., Oct. 1995, pp. 39-43.
10. S. C. M. Cohen and L. N. de Castro, "Data clustering with particle swarms," in Proc. IEEE CEC, 2006, pp. 1792-1798. (Pubitemid 47130713)
11. A. Szabo, A. K. F. Prior, and L. N. de Castro, "The proposal of a velocity memoryless clustering swarm," in Proc. IEEE CEC, Jul. 2010, pp. 1-5.
12. M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen, "Method for increasing the computation speed of an unsupervised learning approach for data clustering," in Proc. IEEE CEC, Jun. 2012, pp. 2957-2964.
13. M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen, "Fast unsupervised learning method for rapid estimation of cluster centroids," in Proc. IEEE CEC, Jun. 2012, pp. 889-896.
14. M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen, "Optimization strategies for rapid centroid estimation," in Proc. 34rd Annu. Int. Conf. IEEE EMBS, Aug.-Sep. 2012, pp. 6212-6215.
15. G. Evers and M. Ben Ghalia, "Regrouping particle swarm optimization: A new global optimization algorithm with improved performance consistency across benchmarks," in Proc. IEEE Int. Conf. Syst., Man, Cybern., Oct. 2009, pp. 3901-3908.
16. X. Li and X. Yao, "Cooperatively coevolving particle swarms for large scale optimization," IEEE Trans. Evol. Comput., vol. 16, no. 2, pp. 210-224, Apr. 2012.
17. M. Yuwono, S. W. Su, B. D. Moulton, and H. T. Nguyen. (2012, Sep.). Rapid Centroid Estimation: An Efficient Particle Swarm Approach for Rapid Optimization of Cluster Centroids [Online]. Available: http://www.mathworks.com/ matlabcentral/fileexchange/38107-rapidcentroi% d-estimation
18. L. Rokach and O. Maimon, "Clustering methods," in The Data Mining and Knowledge Discovery Handbook, New York, USA: Springer, 2005, pp. 321-352.
19. L. Hall, I. Ozyurt, and J. Bezdek, "Clustering with a genetically optimized approach," IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 103-112, Jul. 1999.
20. S. Das and P. Suganthan, "Differential evolution: A survey of the stateof- the-art," IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 4-31, Feb. 2011.
21. C. L. Blake and C. J. Merz. (1998). UCI Repository of Machine Learning Databases [Online]. Available: http://www.ics.uci.edu/mlearn/
22. R. Xu, J. Xu, and D. Wunsch, "A comparison study of validity indices on swarm-intelligence-based clustering," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 42, no. 4, pp. 1243-1256, Aug. 2012.
23. J. C. Dunn, "A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters," J. Cybern. Syst., vol. 3, no. 3, pp. 32-57, 1973.
24. L. Hubert and P. Arabie, "Comparing partitions," J. Classification, vol. 2, no. 1, pp. 193-218, Dec. 1985.
25. T. Calínski and J. Harabasz, "A dendrite method for cluster analysis," Commun. Statist., vol. 3, no. 1, pp. 1-27, 1974.
26. A. Szabo, L. de Castro, and M. Delgado, "The proposal of a fuzzy clustering algorithm based on particle swarm," in Proc. 3rd World Congr. NaBIC, Oct. 2011, pp. 459-465.
27. T. Li, S. Ma, and M. Ogihara, "Entropy-based criterion in categorical clustering," in Proc. ICML, 2004, pp. 536-543. (Pubitemid 40290850)
28. A. K. Jain, M. N. Murty, and P. J. Flynn, "Data clustering: A review," ACM Comput. Surveys (CSUR), vol. 31, no. 3, pp. 264-323, Sep. 1999.
29. N. X. Vinh, J. Epps, and J. Bailey, "Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance," J. Mach. Learning Res., vol. 11, pp. 2837-2854, Dec. 2010.