An algorithm based on density and compactness for dynamic overlapping clustering

Pattern Recognition

Volumn 46, Issue 11, 2013, Pages 3040-3055

  Pérez Suárez, Airel ^a,b   Martínez Trinidad, José Fco ^b   Carrasco Ochoa, Jesús A ^a   Medina Pagola, José E ^a  

^a INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

^b ADVANCED TECHNOLOGIES APPLICATION CENTER   (Cuba)

Author keywords

Clustering; Data mining; Dynamic clustering algorithms; Overlapping clustering algorithms

Indexed keywords

CLUSTERING; DISJOINT CLUSTERS; DYNAMIC CLUSTERING ALGORITHM; OVERLAPPING CLUSTERING; OVERLAPPING CLUSTERS; SOCIAL NETWORK ANALYSIS; STATE-OF-THE-ART ALGORITHMS;

BIOINFORMATICS; DATA MINING; GRAPH THEORY; SOCIAL NETWORKING (ONLINE);

CLUSTERING ALGORITHMS;

EID: 84878873485     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2013.03.022     Document Type: Article

References (49)

1. D. Pfitzner, R. Leibbrandt, and D. Powers Characterization and evaluation of similarity measures for pairs of clusterings Knowledge and Information Systems 19 3 2009 361 394
2. L. Hsiu-Hsia, S. San-Ging, L. Yueh-Huang, and Y. Shyr-Shen Bone age cluster assessment and feature clustering analysis based on phalangeal image rough segmentation Pattern Recognition 45 1 2012 322 332 10.1016/j.patcog.2011. 06.003
3. V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin, and B. Thirion A supervised clustering approach for fMRI-based inference of brain states Pattern Recognition 45 6 2012 2041 2049 10.1016/j.patcog.2011.04.006
4. D. Xiang-Ying, C. Qing-Cai, W. Xiao-Long, X. Jun, Online topic detection and tracking of financial news based on hierarchical clustering, in: Proceedings of the International Conference on Machine Learning and Cybernetics (ICMLC 2010), 2010, pp. 3358-3361.
5. A. Ducournau, A. Bretto, S. Rital, and B. Laget A reductive approach to hypergraph clustering an application to image segmentation Pattern Recognition 45 7 2012 2788 2803 10.1016/j.patcog.2012.01.005
6. A. Narasimhamurthy, D. Greene, N. Hurley, and P. Cunningham Partitioning large networks without breaking communities Knowledge and Information Systems 25 2 2010 345 369
7. I. Maraziotis A semi-supervised fuzzy clustering algorithm applied to gene expression data Pattern Recognition 45 1 2012 637 648 10.1016/j.patcog. 2011.05.007
8. L. Chang, M. Duarte, L. Sucar, E. Morales, Object class recognition using SIFT and Bayesian networks, in: Proceedings of the Mexican International Conference on Artificial Intelligence (MICAI 2010), Lecture Notes in Artificial Intelligence, vol. 6438, 2010, pp. 56-66.
9. R. Abella-Pérez, J. Medina-Pagola, An incremental text segmentation by clustering cohesion, in: Proceedings of the International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions (HaCDAIS 2010), 2010, pp. 65-72.
10. J. Aslam, K. Pelekhov, D. Rus, Static and dynamic information organization with star clusters, in: Proceedings of the 7th International Conference on Information and Knowledge Management, 1998, pp. 208-217.
11. A. Pons-Porrata, J. Ruiz-Shulcloper, R. Berlanga-Llavorí, Y. Santiesteban-Alganza, Un algoritmo incremental para la obtención de cubrimientos con datos mezclados, Reconocimiento de Patrones. Avances y Perspectivas. Research on Computing Science, CIARP2002, 2002, pp. 405-416.
12. E. Amigó, J. Gonzalo, J. Artiles, and F. Verdejo A comparison of extrinsic clustering evaluation metrics based on formal constraints Information Retrieval 12 2009 461 486
13. T. Havens, J. Bezdek, C. Leckie, L. Hall, M. Palaniswami, Fuzzy c-means for very large data, IEEE Transactions on Fuzzy Systems PP (99) (2012) 1-1. http://dx.doi.org/10.1109/TFUZZ.2012.2201485.
14. S. Okada, T. Nishida, Online incremental clustering with distance metric learning for high dimensional data, in: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN), 2011, pp. 2047-2054.
15. H. Ning, W. Xu, Y. Chi, Y. Gong, and T. Huang Incremental spectral clustering by efficiently updating the eigen-system Pattern Recognition 43 2010 113 127
16. L. Tao, S. Anand, HIREL: an incremental clustering algorithm for relational datasets, in: Proceedings of the 8th IEEE International Conference on Data Mining, 2008, pp. 887-892.
17. J. Correa-Morris, D. Espinosa-Isidrón, and D. Álvarez Nadiozhin An incremental nested partition method for data clustering Pattern Recognition 43 2010 2439 2455
18. S. Guha, N. Mishra, R. Motwani, L. O'Callaghan, Clustering data streams, in: Proceedings of the IEEE FOCS Conference, 2000, pp. 359-366.
19. L. O'Callaghan, N. Mishra, A. Meyerson, S. Guha, R. Motwani, Streaming-data algorithms for high-quality clustering, in: Proceedings of the 18th International Conference on Data Engineering (ICDE '02), 2002, pp. 685-709.
20. C. Aggarwal, J. Han, J. Wang, P. Yu, A framework for clustering evolving data streams, in: Proceedings of the 29th International Conference on Very Large Data Bases, VLDB'2003, VLDB Endowment, 2003, pp. 81-92, URL .
21. D. Chakrabarti, R. Kumar, A. Tomkins, Evolutionary clustering, in: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 554-560.
22. Y. Chi, X. Song, D. Zhou, K. Hino, B. Tseng, Evolutionary spectral clustering by incorporating temporal smoothness, in: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'07, ACM, New York, NY, USA, 2007, pp. 153-162, http://dx.doi.org/http://doi.acm. org/10.1145/1281192.1281212.
23. A. Ahmed, E. Xing, Dynamic non-parametric mixture models and the recurrent chinese restaurant process: with applications to evolutionary clustering, in: Proceedings of the Eighth SIAM International Conference on Data Mining (SDM2008), 2008.
24. O. Zamir, O. Etziony, Web document clustering: a feasibility demonstration, in: Proceedings of the 21st Annual International ACM SIGIR Conference, 1998, pp. 46-54.
25. K. Hammouda, and M. Kamel Efficient phrase-based document indexing for web document clustering IEEE Transactions on Knowledge and Data Engineering 16 10 2004 1279 1296
26. A. Pérez-Suárez, J. Martínez-Trinidad, J. Carrasco-Ochoa, J. Medina-Pagola, A new incremental algorithm for overlapped clustering, in: Proceedings of the 14th Iberoamerican Congress on Pattern Recognition (CIARP2009), Lecture Notes in Computer Sciences, vol. 5856, 2009, pp. 497-504.
27. A. Pérez-Suárez, J. Martínez-Trinidad, J. Carrasco-Ochoa, and J. Medina-Pagola A dynamic clustering algorithm for building overlapping clusters Intelligent Data Analysis 16 2 2012 211 232
28. R. Gil-García, and A. Pons-Porrata Dynamic hierarchical algorithms for document clustering Pattern Recognition Letters 31 6 2010 469 477
29. D. Duan, Y. Li, R. Li, and Z. Lu Incremental K-clique clustering in dynamic social networks Artificial Intelligence Review 38 2012 129 147
30. S. Gregory, An algorithm to find overlapping community structure in networks, in: Proceedings of the PKDD 2007, Lecture Notes in Artificial Intelligence, vol. 4702, 2007, pp. 91-102.
31. S. Gregory, A fast algorithm to find overlapping communities in networks, in: Proceedings of the 12th ECML KDD, Lecture Notes in Artificial Intelligence, vol. 5212, 2008, pp. 408-423.
32. D. Gusfield Algorithms on Strings, Trees and Sequences Computer Science and Computational Biology 1997 Cambridge University Press (Chapter 6)
33. R. Sibson An optimally efficient algorithm for the single link cluster method Computer Journal 16 1973 30 34
34. G. Salton, A. Wong, and C. Yang A vector space model for automatic indexing Communications of the ACM 18 11 1975 613 620 http://dx.doi.org/doi: http://doi.acm.org/10.1145/361219.361220
35. M. Berry Survey of Text Mining, Clustering, Classification and Retrieval 2004 Springer-Verlag
36. G. Palla, I. Derényi, I. Farkas, and T. Vicsek Uncovering the overlapping community structure of complex networks in nature and society Nature 435 7043 2005 814 824
37. T. Jo, M. Lee, The evaluation measure of text clustering for the variable number of clusters, in: Proceedings of the 4th International Symposium on Neural Networks: Part II - Advances in Neural Networks, 2007, pp. 871-879.
38. R. Gil-García, J. Badía-Contelles, A. Pons-Porrata, Extended star clustering algorithm, in: Proceedings of the 8th Iberoamerican Congress on Pattern Recognition (CIARP2003), Lecture Notes in Computer Science, vol. 2905, 2003, pp. 480-487.
39. A. Pérez-Suárez, J. Medina-Pagola, A clustering algorithm based on generalized stars, in: Proceedings of the 5th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2007), Lecture Notes in Artificial Intelligence, vol. 4571, 2007, pp. 248-262.
40. A. Gago-Alonso, A. Pérez-Suárez, J. Medina-Pagola, ACONS: a new algorithm for clustering documents, in: Proceedings of the 12th Iberoamerican Congress on Pattern Recognition (CIARP2007), Lecture Notes in Computer Science, vol. 4756, 2007, pp. 664-673.
41. E. Greengrass, Information Retrieval: A Survey, Technical Report TR-R52-008-001, 2001.
42. Y. Zhao, G. Karypis, Criterion Functions for Document Clustering: Experiments and Analysis, Technical Report 01-40, Department of Computer Science, University of Minnesota, Minneapolis, MN, 2001.
43. B. Larsen, and C. Aone Fast and effective text mining using linear-time document clustering Knowledge Discovery and Data Mining 1999 16 22
44. M. Halkidi, Y. Batistakis, and M. Vazirgiannis On clustering validation techniques Journal of Intelligent Information Systems 17 2-3 2001 107 145
45. M. Steinbach, G. Karypis, V. Kumar, A comparison of document clustering techniques, in: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), 2000.
46. J. Bakus, M. Hussin, M. Kamel, A SOM-based document clustering using phrases, in: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02), 2002, pp. 2212-2216.
47. 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, pp. 410-420.
48. A. Bagga, B. Baldwin, Entity-based cross-document coreferencing using the vector space model, in: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, 1998, pp. 79-85.
49. Y. Zhao, G. Karypis, and U. Fayyad Hierarchical clustering algorithms for document datasets Data Mining and Knowledge Discovery 10 2 2005 141 168