A quality driven hierarchical data divisive soft clustering for information retrieval

Knowledge-Based Systems

Volumn 26, Issue , 2012, Pages 9-19

  Bordogna, Gloria ^a   Pasi, Gabriella ^b  

^a CNR Consiglio Nazionale delle Ricerche   (Italy)

^b UNIVERSITY OF MILANO BICOCCA   (Italy)

Author keywords

Cluster's quality; Document clustering; Fuzzy C Means; Quality measures; Soft Hierarchical Clustering

Indexed keywords

CARDINALITIES; DOCUMENT CLUSTERING; FUZZY C MEAN; HIER-ARCHICAL CLUSTERING; HIERARCHICAL DATA; HIERARCHICAL FUZZY; HIERARCHICAL LEVEL; ITS EVALUATION; K-MEANS ALGORITHM; MEMBERSHIP DEGREES; MULTIPLE DIMENSIONS; QUALITY ASSESSMENT; QUALITY MEASURES; QUALITY VALUE; SOFT CLUSTERING; SUB-CLUSTERS; VARIABLE NUMBER;

FUZZY CLUSTERING; FUZZY SYSTEMS; INFORMATION RETRIEVAL; QUALITY CONTROL;

CLUSTERING ALGORITHMS;

EID: 84155189174     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.06.012     Document Type: Article

References (55)

1. C. Back, and M. Hussain Validity measures for fuzzy partitions H. Bock, W. Polasek, Data analysis and Information Systems 1995 Springer Verlag 114 125
2. R. Baeza-Yates, and B. Ribeiro-Neto Modern Information Retrieval 1999 Addison/Wesley/Longman Publishing Co.
3. J.C. Bezdek Pattern Recognition with Fuzzy Objective Function Algorithms 1981 Plenum Press New York
4. D. Boley Principal direction divisive partitioning Data Mining and Knowledge Discovery 2 1998 325 344 (Pubitemid 128695482)
5. G. Bordogna, M. Pagani, and G. Pasi Incremental hierarchical fuzzy clustering for category-based news filtering B. Bouchon-Meunier, C. Marsala, M. Rifqi, R.R. Yager, Uncertainty and Intelligent Information Systems 2008 World Scientific
6. G. Bordogna, G. Pasi, Hypersferical hierarchical fuzzy clustering of documents for information retrieval, in: Proceedings of the IEEE/WIC/ACM Intern. Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2009), Milan, 2009.
7. P. Cheeseman, and J. Stutz Bayesian classification (AutoClass): theory and results Advances in Knowledge Discovery and Data Mining 1996 ACM press 153 180
8. H. Chen, S. Dumais, Bringing order to the web: automatically categorizing search results, in: Proceedings of the SIGCHI Conference on Human factors computing systems, IR-76, 2000, pp. 145-152.
9. S.M. Chen, and L.Y. Chen A fuzzy hierarchical clustering method for clustering documents based on dynamic cluster centers Journal of the Chinese Institute of Engineering 30 1 2007 169 172
10. Copenhagen 2009, Climate Conference in Copenhagen. , 2009 (seen on the 30.12.2009).
11. D.R. Cutting, D.R. Karger, J.O. Pedersen, and J.W. Tukey Scatter/gather: a cluster-based approach to browsing large document collections N. Belkin, Proceedings of the 15th Annual International Conference on SIGIR '92 vol. 92 1992 ACM Press New York, NY 318 329
12. R. Dav, and R. Krishnapuram Robust clustering methods: a unified view IEEE Transactions on Fuzzy Systems 5 2 1997 270 293 (Pubitemid 127768514)
13. F. Debole, F. Sebastiani, Supervised term weighting for automated text categorization, in: Proceedings of the 18th ACM Symposium on Applied Computing, 2003, pp. 784-788.
14. A.P. Dempster, N.M. Laird, and D.B. Rubin Maximum likelihood from incomplete data via the EM algorithm Journal of the Royal Statistical Society, Series B 39 1 1977 1 38
15. Y. Dong, Y. Zhuang, Fuzzy Hierarchical clustering algorithm facing large databases, in: Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, China, 2004, pp. 4282-4286.
16. J.C. Dunn A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters Journal of Cybernetics 3 1973 32 57
17. C. Fraley Algorithms for Model-based Gaussian Hierarchical Clustering vol. 20 1998 SIAM Journal on Scientific Computing
18. Y. Fukuyama, M. Sugeno, A new method of choosing the number of clusters for the fuzzy C-means method, in: Proceedings of Fifth Fuzzy Systems Symposium, 1989, pp. 247-250.
19. P. Groenen, U. Kaymak, and J. van Rosmalen Fuzzy clustering with Minkowski distance functions V.J. Oliveira, W. Pedrycz, Fuzzy Clustering and its Applications 2007 Wiley 53 68
20. R.J. Hathaway, J.C. Bezdek, and Y. Hu Generalized fuzzy C-means clustering strategies using Lp norm distances IEEE Transactions on Fuzzy Systems 8 5 2000 576 582
21. T. Hofmann, The cluster-abstraction model: unsupervised learning of topic hierarchies from text data, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1999), 1999, pp. 682-687.
22. F. Höppner, F. Klawonn, R. Kruse, and T. Runkler Fuzzy Cluster Analysis 1999 Wiley
23. Y.J. Horng, S.M. Chen, Y.C. Chang, and C.H. Lee A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques IEEE Transactions on Fuzzy Systems 13 2 2005 216 228
24. U. Kaymak, and M. Setnes Fuzzy clustering with volume prototypes and adaptive clustering merging IEEE Transactions on Fuzzy Systems 10 6 2002 705 712
25. G.J. Klir, and T.A. Folger Fuzzy Sets, Uncertainty and Information 1988 Prentice Hall
26. D. Kraft, J. Chen, M.J. Martin-Bautista, and M.A. Vila Textual information retrieval with user profiles using fuzzy clustering and inferencing P. Szczepaniak, J. Segovia, J. Kacprzyk, L.A. Zadeh, Intelligent Exploration of the Web Studies in Fuzziness and Soft Comp. Series vol. 111 2003 Physica Verlag 201 215
27. R. Krishnapuram, and J.M. Keller A possibilistic approach to clustering IEEE Transactions on Fuzzy Systems 1 1993 98 110
28. A.K. Jain, M.N. Murty, and P.J. Flynn Data clustering: a review ACM Computing Surveys 31 3 1999 264 323
29. A.V. Leouski, W.B. Croft, An evaluation of techniques for clustering search results. Technical Report of the Department of Computer Science University of Massachusetts at Amherst, IR-76:122-133, 1996.
30. J. Li, X. Gao, and L. Jiao A fuzzy CLOPE algorithm and its optimal parameter choice Journal of Electronics (China) 23 3 2006 383 388
31. N. Pal, and J. Bezdek On cluster validity for the fuzzy C-means model IEEE Transactions on Fuzzy Systems 3 3 1995 370 390
32. W. Pedrycz Clustering and fuzzy clustering Knowledge-based Clustering 2005 Wiley and Son
33. C. Posse Hierarchical model-based clustering for large datasets Journal of Computational and Graphical Statistics 10 3 2001 464 486
34. W.M. Rand Objective criteria for the evaluation of clustering methods Journal of the American Statistical Association 66 1971 846 850
35. M.R. Rezaee, B.P.F. Lelieveldt, and J.H.C. Reiber A new cluster validity index for the fuzzy C-mean Pattern Recognition Letters 19 3-4 1998 237 246 (Pubitemid 128383121)
36. H. Rhee, K. Oh, A validity measure for fuzzy clustering and its use in selecting optimal number of clusters, in: Proceedings of the 5th IEEE International Conference on Fuzzy Systems, vol. 2, 1996, pp. 1020-1025.
37. M.M. Rodrigues, L. Sacks, A scalable hierarchical fuzzy clustering algorithm for text mining, in: Proceedings of the 4th International Conference on Recent Advances in Soft Computing, RASC'2004, 2004, pp. 269-274.
38. R.C. Romero-Zaliz, C. Rubio-Escudero, J.P. Cobb, F. Herrera, O. Cordon, and I. Zwir A multiobjective evolutionary conceptual clustering methodology for gene annotation within structural databases: a case of study on the gene ontology database IEEE Transacations on Evolutionary Computation 12 6 2008 679 701
39. T.A. Runkler, and J.C. Bezdek Web mining with relational clustering International Journal of Approximate Reasoning 32 2-3 2003 217 236
40. G. Salton, and M.J. McGill Introduction to Modern Information Retrieval 1984 McGraw-Hill
41. S.M. Savaresi, D.L. Boley, S. Bittanti, G. Gazzaniga, Choosing the cluster to split in bisecting divisive clustering algorithms, in: 2nd SIAM International Conference on Data Mining, 2009, pp. 299-314.
42. M. Sollenborn, P. Funk, Category-based filtering and user stereotype cases to reduce the latency problem in recommender systems, in: Proceedings of the 6th European Conference on Case Based Reasoning, ECCBR2002, 2002, pp. 395-405.
43. K.A. Sparck Jones A statistical interpretation of term specificity and its application in retrieval Journal of Documentation 28 1 1972 11 20
44. M. Steinbach, G. Karypis, V. Kumar, A Comparison of document clustering techniques, in: Proceeding of World Text Mining Conference, in Proceedings of the KDD-2000 Workshop on Text Mining, 2000, pp. 109-111.
45. H. Sun, S. Wang, and O. Jiang FCM-based model selection algorithms for determining the number of clusters Pattern Recognition 37 10 2004 2027 2037
46. A. Szymkowiak, J. Larsen, L.K. Hansen, Mathematical modeling, Hierarchical clustering for data mining, in: Proceedings of the Fifth International Conference on Knowledge-Based Intelligent Information Engineering Systems & Allied Technologies, 2001, pp. 261-265.
47. V. Torra, Fuzzy c-means for fuzzy hierarchical clustering, in: Proceedings of the 14th IEEE International Conference on Fuzzy Systems, 2005, pp. 646-651. (Pubitemid 41189583)
48. V. Torra, S. Lanau, S. Miyamoto, Fuzzy clustering for indexing in the GAMBAL information retrieval system, in: Proceedings of the European Conference on Fuzzy Sets and Technologies (EUSFLAT 2003) Zittau, Germany, 2003, pp. 54-58.
49. L.H. Ungar, and D.P. Foster Clustering methods for collaborative filtering Proceedings of the Workshop on Recommendation Systems 1998 AAAI Press Menlo Park California
50. S. Vaithyanathan, B. Dom, Model-based hierarchical clustering, in: Proceedings of the 16th Conf. Uncertainty in Artificial Intelligence, 2000, pp. 599-608.
51. P. Willett Recent trends in hierarchic document clustering: a critical review Information Processing& Management 24 5 1988 577 597
52. R.R. Yager Default knowledge and measures of specificity Information Sciences 61 1992 1 44
53. L. Zadeh Fuzzy sets Information and Control 8 1965 338 353
54. Y. Zhao, and G. Karypis Hierarchical clustering algorithms for document datasets Data mining and Knowledge Discovery 10 2005 141 168 (Pubitemid 41217849)
55. H. Zhang, and J. Lu Semi-supervised fuzzy clustering: a kernel-based approach Knowledge-based Systems 22 6 2009 477 481