Performance evaluation of density-based clustering methods

Information Sciences

Volumn 179, Issue 20, 2009, Pages 3583-3602

  Aliguliyev, Ramiz M ^a  

^a INSTITUTE OF PHYSICS   (Azerbaijan)

Author keywords

Density based clustering methods; Modified DE algorithm; Partitional clustering; Text mining; Validity indices

Indexed keywords

DENSITY-BASED CLUSTERING METHODS; MODIFIED DE ALGORITHM; PARTITIONAL CLUSTERING; TEXT MINING; VALIDITY INDICES;

CLUSTER ANALYSIS; CONVERGENCE OF NUMERICAL METHODS; EVOLUTIONARY ALGORITHMS; FUNCTION EVALUATION; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; INFORMATION SERVICES; MINING; QUALITY CONTROL; WORLD WIDE WEB;

CLUSTERING ALGORITHMS;

EID: 67849118587     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2009.06.012     Document Type: Article

References (56)

1. A. Abraham, S. Das, A. Konar, Document clustering using differential evolution, in: Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), Springer, Berlin, 2006, pp. 1784-1791.
2. Alguliev R.M., and Alyguliev R.M. Automatic text documents summarization through sentences clustering. Journal of Automation and Information Sciences 40 (2008) 53-63
3. Alguliev R.M., Alyguliev R.M., and Bagirov A.M. Global optimization in the summarization of text documents. Automatic Control and Computer Sciences 39 (2005) 42-47
4. Alguliev R.M., and Aliguliyev R.M. Fast genetic algorithm for clustering of text documents. Artificial Intelligence 3 (2005) 698-707 (in Russian)
5. R.M. Aliguliyev, A novel partitioning-based clustering method and generic document summarization, in: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops) (WI-IATW'06), Hong Kong, China, 2006, pp. 626-629.
6. Aliguliyev R.M. A clustering method for document collections and algorithm for estimation the optimal number of clusters. Artificial Intelligence 4 (2006) 651-659 (in Russian)
7. Aliguliyev R.M. Automatic document summarization by sentence extraction. Journal of Computational Technologies 12 (2007) 5-15
8. In: Allan J. (Ed). Topic detection and tracking: event-based information organization (2002), Kluwer Academic Publishers, Norwell, USA
9. Azzag H., Venturini G., Oliver A., and Guinot C. A hierarchical ant based clustering algorithm and its use in three real-world applications. European Journal of Operational Research 179 (2007) 906-922
10. Baeza-Yates R., and Ribeiro-Neto R. Modern Information Retrieval (1999), Addison Wesley, ACM Press, New York
11. Bagirov A.M. Modified global k-means algorithm for minimum sum-of-squares clustering problems. Pattern Recognition 41 (2008) 3192-3199
12. Bandyopadhyay S., and Saha S. A point symmetry-based clustering technique for automatic evolution of clusters. IEEE Transactions on Knowledge and Data Engineering 20 (2008) 1441-1457
13. Bezdek J.C., and Pal N.R. Some new indexes of cluster validity. IEEE Transactions on Systems, Man and Cybernetics - Part B: Cybernetics 28 (1998) 301-315
14. F. Boutin, M. Hascoet, Cluster validity indices for graph partitioning, in: Proceedings of the Eighth International Conference on Information Visualization (IV 2004), London, UK, 2004, pp. 376-381.
15. Y. Chen, J. Bi, Clustering by maximizing sum-of-squared separation distance, in: Proceedings of the Workshop on Clustering High Dimensional Data and its Applications, Newport Beach, USA, 2005, pp. 1-12.
16. Y. Chen, L. Tu, Density-based clustering for real-time stream data, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'07), San Jose, USA, 2007, pp. 133-142.
17. Chou C.H., Su M.C., and Lai E. A new cluster validity measure and its application to image compression. Pattern Analysis and Applications 7 (2004) 205-220
18. Das S., and Konar A. Automatic image pixel clustering with an improved differential evolution. Applied Soft Computing 9 (2009) 226-236
19. Das S., Abraham A., and Konar A. Automatic clustering using an improved differential evolution algorithm. IEEE Transaction on Systems, Man, and Cybernetics - Part A: Systems and Humans 38 (2008) 218-237
20. Das S., Abraham A., and Konar A. Automatic clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognition Letters 29 (2008) 688-699
21. I.S. Dhillon, Y. Guan, B. Kulis, A unified view of kernel k-means, spectral clustering and graph cuts, University of Texas UTCS Technical Report #TR-04-25, 2005, 20 p.
22. Dunlavy D.M., O'Leary D.P., Conroy J.M., and Schlesinger J.D. QCS: a system for querying clustering and summarizing documents. Information Processing and Management 43 (2007) 1588-1605
23. Dubes R., and Jain A.K. Validity studies in clustering methodologies. Pattern Recognition 11 (1979) 235-254
24. Friedman M., Last M., Makover Y., and Kandel A. Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology. Information Sciences 177 (2007) 467-475
25. Grabmeier J., and Rudolph A. Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery 6 (2002) 303-360
26. Halkidi M., Batistakis Y., and Vazirgiannis M. On clustering validation techniques. Journal of Intelligent Systems 17 (2001) 107-145
27. Hammouda K.M., and Kamel M.S. Efficient phrase-based document indexing for web document clustering. IEEE Transactions on Knowledge and Data Engineering 16 (2004) 1279-1296
28. E.-H. Han, D. Boley, M. Gini, R. Gross, K. Hastings, G. Karypis, V. Kumar, B. Mobasher, J. Moore, WebACE: a web agent for document categorization and exploration, in: Proceedings of the Second International Conference on Autonomous Agents, Minneapolis, MN, USA, 1998, pp. 408-415.
29. Han J., and Kamber M. Data Mining: Concepts and Techniques. second ed. (2006), Morgan Kaufman, San Francisco
30. A. Huang, Similarity measures for text document clustering, in: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC2008), Christchurch, New Zealand, 2008, pp. 49-56.
31. Huang J.Z., Ng M.K., Rong H., and Li Z. Automated variable weighting in k-means type clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005) 657-668
32. Jain A.K., Murty M.N., and Flynn P.J. Data clustering: a review. ACM Computing Surveys 31 (1999) 264-323
33. Kalashnikov D.V., Chen Z.S., Mehrotra S., and Nuray-Turan R. Web people search via connection analysis. IEEE Transactions on Knowledge and Data Engineering 20 (2008) 1550-1565
34. Khan M.S., and Khor S.W. Web document clustering using a hybrid neural network. Applied Soft Computing 4 (2004) 423-432
35. Korenius T., Laurikkala J., and Juhola M. On principal component analysis, cosine and Euclidean measures in information retrieval. Information Sciences 177 (2007) 4893-4905
36. Laszlo M., and Mukherjee S. A genetic algorithm that exchanges neighboring centers for k-means clustering. Pattern Recognition Letters 28 (2007) 2359-2366
37. Lee C.H., Zaiane O.R., Park H.H., Huang J., and Greiner R. Clustering high dimensional data: a graph-based relaxed optimization approach. Information Sciences 178 (2008) 4501-4511
38. Li Y., Chung S.M., and Holt J.D. Text document clustering based on frequent word meaning sequences. Data and Knowledge Engineering 64 (2008) 381-404
39. T. Li, C. Ding, Weighted consensus clustering, in: Proceedings of the SIAM International Conference on Data Mining (SDM 2008), Atlanta, USA, 2008, pp. 798-809.
40. Li Y., Luo C., and Chung S.M. Text clustering with feature selection by using statistical data. IEEE Transactions on Knowledge and Data Engineering 20 (2008) 641-652
41. Mirkin B. Mathematical Classification and Clustering (1996), Kluwer Academic Press, Boston, Dordrecht
42. Nguyen C.D., and Cios K.J. GAKREM: a novel hybrid clustering algorithm. Information Sciences 178 (2008) 4205-4227
43. Patrikainen A., and Meila M. Comparing subspace clusterings. IEEE Transactions on Knowledge and Data Engineering 18 (2006) 902-916
44. Porter M. An algorithm for suffix stripping. Program 14 (1980) 130-137
45. Price K.V., Storn R.M., and Lampinen J.A. Differential evolution: a practical approach to global optimization (natural computing series) (2005), Springer-Verlag, Berlin
46. A. Rosenberg, J. Hirschberg, V-measure: a conditional entropy-based external cluster evaluation measure, in: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), Prague, Czech Republic, 2007, pp. 410-420.
47. Rubinov A.M., Soukhorukova N.V., and Ugon J. Classes and clusters in data analysis. European Journal of Operational Research 173 (2006) 849-865
48. Tan M.P., Broach J.R., and Floudas C.A. A novel clustering approach and prediction of optimal number of clusters: global optimum search with enhanced positioning. Journal of Global Optimization 39 (2007) 323-346
49. F. Wang, C. Zhang, T. Li, Regularized clustering for documents, in: Proceedings of the 30th Annual International ACM SIGIR Conference (SIGIR'07), Amsterdam, The Netherlands, 2007, pp. 95-102.
50. Zhang Y., Wang W., Zhang X., and Li Y. A cluster validity index for fuzzy clustering. Information Sciences 178 (2008) 1205-1218
51. Zhao Y., and Karypis G. Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning 55 (2004) 311-331
52. ftp://ftp.cs.cornell.edu/pub/smart/english.stop.
53. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/webkb-data.gtar.gz.
54. www.daviddlewis.com/resources/testcollections/reuters21578.
55. http://people.csail.mit.edu/jrennie/20Newsgroups/.
56. http://trec.nist.gov.