Particle swarm optimizer for variable weighting in clustering high-dimensional data

Machine Learning

Volumn 82, Issue 1, 2011, Pages 43-70

  Lu, Yanping ^a,b   Wang, Shengrui ^a   Li, Shaozi ^b   Zhou, Changle ^b  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

^b XIAMEN UNIVERSITY   (China)

Author keywords

High dimensional data; Particle swarm optimization; Projected clustering; Text clustering; Variable weighting

Indexed keywords

HIGH DIMENSIONAL DATA; PARTICLE SWARM; PROJECTED CLUSTERING; TEXT CLUSTERING; VARIABLE WEIGHTING;

CLUSTER ANALYSIS; PARTICLE SWARM OPTIMIZATION (PSO);

CLUSTERING ALGORITHMS;

EID: 78650700300     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-009-5154-2     Document Type: Article

References (36)

1. R. Agrawal J. Gehrke D. Gunopulos P. Raghavan 2005 Automatic subspace clustering of high dimensional data Data Mining and Knowledge Discovery 11 1 5 33 10.1007/s10618-005-1396-1 2119481 (Pubitemid 41202921)
2. E. Aitnouri S. Wang D. Ziou 2000 On comparison of clustering techniques for histogram pdf estimation Pattern Recognition and Image Analysis 10 2 206 217
3. D. Boley M. Gini, et al. 1999 Document categorization and query generation on the world wild web using WebACE AI Review 11 365 391
4. M. Bouguessa S. Wang H. Sun 2006 An objective approach to cluster validation Pattern Recognition Letters 27 13 1419 1430 10.1016/j.patrec.2006.01. 015 (Pubitemid 44037102)
5. C. Domeniconi D. Gunopulos S. Ma B. Yan M. Al-Razgan D. Papadopoulos 2007 Locally adaptive metrics for clustering high dimensional data Data Mining and Knowledge Discovery Journal 14 63 97 10.1007/s10618-006-0060-8 2359173 (Pubitemid 46345356)
6. Eberhart, R. C., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proc. 6th international symposium on micromachine and human science, Japan (pp. 39-43).
7. Elke, A., Christian, B., et al. (2008). Detection and visualization of subspace cluster hierarchies. In LNCS (Vol. 4443, pp. 152-163). Berlin: Springer.
8. Evett, I. W., & Spiehler, E. J. (1987). Rule induction in forensic science. Central Research Establishment. Home Office Forensic Science Service, Aldermaston, Reading, Berkshire RG7 4PN.
9. Goil, G. S., Nagesh, H., & Choudhary, A. (1999). Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report CPDC-TR-9906-010, Northwestern University.
10. Han, E. H., Boley, D., et al. (1998). WebACE: A web agent for document categorization and exploration. In Proc. of 2nd international conf. on autonomous agents.
11. Handl, J., & Knowles, J. (2004). Multiobjective clustering with automatic determination of the number of clusters. Technical Report, UMIST, Department of Chemistry.
12. J. Z. Huang M. K. Ng H. Rong Z. Li 2005 Automated dimension weighting in k-means type clustering IEEE Transactions on Pattern Analysis and Machine Intelligence 27 5 1 12 1131.30330 10.1109/TPAMI.2005.95 (Pubitemid 40608400)
13. L. Jing M. K. Ng J. Z. Huang 2007 An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data IEEE Transactions on Knowledge and Data Engineering 19 8 1026 1041 10.1109/TKDE.2007.1048 (Pubitemid 47000341)
14. Kriegel, H.-P., Kroger, P., & Zimek, A. (2009). Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM Transactions on Knowledge Discovery Data, 3(1), Article 1.
15. J. J. Liang A. K. Qin P. N. Suganthan S. Baskar 2006 Comprehensive learning particle swarm optimizer for global optimization of multimodal functions IEEE Transactions on Evolutionary Computation 10 3 281 295 10.1109/TEVC.2005.857610 (Pubitemid 43821759)
16. Lu, Y. (2009). Particle swarm optimizer: applications in high-dimensional data clustering. Ph.D. Dissertation, University of Sherbrooke, Department of Computer Science.
17. V. Makarenkov P. Legendre 2001 Optimal variable weighting for ultrametric and additive trees and k-means partitioning: Methods and software Journal of Classification 18 2 245 271 1040.91087 1868631 (Pubitemid 33587487)
18. O. L. Mangasarian W. H. Wolberg 1990 Breast cancer diagnosis via linear programming SIAM News 23 5 1 18
19. Moise, G., & Sander, J. (2008). Finding non-redundant, statistically significant regions in high dimensional data: A novel approach to projected and subspace clustering. In Proc. of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 533-541).
20. G. Moise J. Sander M. Ester 2008 Robust projected clustering Knowledge and Information Systems 14 3 273 298 1161.68770 10.1007/s10115-007-0090-6
21. G. C. Onwubolu M. Clerc 2004 Optimal operating path for automated drilling operations by a new heuristic approach using particle swarm optimization International Journal of Production Research 42 3 473 491 1176.90178 10.1080/00207540310001614150
22. Pang-ning, T., Michael, S., & Vipin, K. (2006). Introduction to data mining (p. 77). Upper Saddle River: Pearson Education.
23. L. Parsons E. Haque H. Liu 2004 Subspace clustering for high dimensional data: A review SIGKDD Explorations Newsletter 6 90 105 10.1145/1007730.1007731
24. M. F. Porter 1980 An algorithm for suffix stripping Program 14 3 130 137
25. Procopiuc, C. M., Jones, M., Agarwal, P. K., & Murali, T. M. (2002). A Monte Carlo algorithm for fast projective clustering. In Proc. of ACM SIGMOD international conference on management of data (pp. 418-427).
26. A. Salman I. Ahmad S. Al-Madani 2003 Particle swarm optimization for task assignment problem Microprocessors and Microsystems 26 8 363 371 10.1016/S0141-9331(02)00053-4
27. G. Salton C. Buckley 1988 Term-weighting approaches in automatic text retrieval Information Processing & Management 24 5 513 523 10.1016/0306-4573(88)90021-0
28. Tasgetiren, M. F., Sevkli, M., Liang, Y.-C., & Gencyilmaz, G. (2004). Particle swarm optimization algorithm for single machine total weighted tardiness problem. In Proc. of the 2004 congress on evolutionary computation (CEC'04), Portland, Oregon (pp. 1412-1419).
29. TREC (1999). Text retrieval conference, http://trec.nist.gov/.
30. Tibshirani, R., Walther, G., et al. (2000). Estimating the number of clusters in a dataset via the Gap Statistic. Technical Report, Stanford Univeristy.
31. F. Van den Bergh A. P. Engelbecht 2000 Cooperative learning in neural networks using particle swarm optimizers South African Computer Journal 26 84 90
32. van der Putten, P., & van Someren, M. (Eds.) (2000). CoIL challenge 2000: the insurance company case. Published by Sentient Machine Research, Amsterdam. Technical Report.
33. K.-G. Woo J.-H. Lee M.-H. Kim Y.-J. Lee 2004 FINDIT: A fast and intelligent subspace clustering algorithm using dimension voting Information and Software Technology 46 4 255 271 10.1016/j.infsof.2003.07.003
34. H. Yoshida K. Kawata Y. Fukuyama Y. Nakanishi 2000 A particle swarm optimization for reactive power and voltage control considering voltage security assessment IEEE Transactions on Power Systems 15 4 1232 1239 10.1109/59.898095 (Pubitemid 32870262)
35. Zhao, Y., & Karypis, G. (2001). Criterion functions for document clustering: Experiments and analysis. Technical Report, CS Dept., Univ. of Minnesota.
36. Zhou, X., Hu, X., Zhang, X., Lin, X., & Song, I.-Y. (2006). Context-sensitive semantic smoothing for the language modeling approach to genomic IR. In SIGIR'06.