An efficient Particle Swarm Optimization approach to cluster short texts

Information Sciences

Volumn 265, Issue , 2014, Pages 36-49

  Cagnina, Leticia ^a   Errecalde, Marcelo ^a   Ingaramo, Diego ^a   Rosso, Paolo ^b  

^a UNIVERSIDAD NACIONAL DE SAN LUIS   (Argentina)

^b UNIVERSITAT POLITÈCNICA DE VALÈNCIA   (Spain)

Author keywords

Clustering; Particle Swarm Optimization; Short text corpora

Indexed keywords

CLUSTERING; COMMERCIAL PRODUCTS; DISCRETE PARTICLE SWARM OPTIMIZATION; MUTATION OPERATORS; ON-LINE INFORMATION; PARTICLE SWARM OPTIMIZATION APPROACHES; SHORT-TEXT CORPORA; SIGNIFICANT DETERIORATIONS;

ABSTRACTING;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84893727984     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.12.010     Document Type: Article

References (53)

1. M. Alexandrov, A. Gelbukh, and P. Rosso An approach to clustering abstracts A. Montoyo, R. Muñoz, E. Métais, Natural Language Processing and Information Systems Lecture Notes in Computer Science vol. 3513 2005 Springer Berlin/Heidelberg 1 10
2. S. Banerjee, K. Ramanathan, and A. Gupta Clustering short texts using wikipedia Proc. of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2007) 2007 ACM New York, NY, USA 787 788
3. P. Berkhin, Survey Of Clustering Data Mining Techniques, Technical Report, Accrue Software, 2002.
4. W. Bin, and S. Zhongzhi A clustering algorithm based on swarm intelligence Proc. of the Int. Conf. on Info-tech and Info-net, ICII 2001 vol. 3 2001 IEEE 58 66
5. L. Cagnina, M. Errecalde, D. Ingaramo, and P. Rosso A discrete particle swarm optimizer for clustering short-text corpora Proc. International Conference on Bioinspired Optimization Methods and their Applications, BIOMA 2008 2008 Jozef Stefan Institute 93 103
6. L. Cagnina, S. Esquivel, and R. Gallard Particle swarm optimization for sequencing problems: a case study Congress on Evolutionary Computation 2004 IEEE Press Portland, Oregon, USA
7. P. Cheeseman, and J. Stutz Bayessian classification (autoclass): theory and results P.S.U. Fayyad, G. Piatetsky-Shapiro, R. Uthurusamy, Advances in Knowledge Discovery and Data Mining 1996 American Association for Artificial Intelligence Menlo Park CA, USA 153 180
8. X. Cui, T. Potok, and P. Palathingal Document clustering using particle swarm optimization Proc. of IEEE Swarm Intelligence Symposium, SIS 2005 2005 IEEE Press
9. R. Eberhart, and J. Kennedy A new optimizer using particle swarm theory Proc. of the Sixth International Symposium on Micro Machine and Human Science, MHS'95 1995 IEEE Press Nagoya, Japan 39 43
10. M. Errecalde, D. Ingaramo, P. Rosso, Proximity estimation and hardness of short-text corpora, in: Proc. of 5th Int. Workshop on Text-based Information Retrieval, TIR 2008, IEEE CS, 2008, pp. 15-19.
11. â̂-: an effective iterative method for short-text clustering tasks Proc. of the 23rd Int. Conf. on Industrial Engineering and other Applications of Applied Intelligent Systems, IEA/AIE 2010 2010 Springer-Verlag Berlin, Heidelberg 550 559
12. G. Fasano, and A. Franceschini A multidimensional version of the Kolmogorov-Smirnov test Mon. Notices Roy. Astron. Soc. 225 1987 155 170
13. A. Feng Document clustering - an optimization problem ACM Conf. on Research and Development in Information Retrieval 2007 ACM 819 820
14. D. Fisher Knowledge acquisition via incremental conceptual clustering Mach. Learn. 2 1987 139 172
15. R. Forsati, M. Mahdavi, M. Shamsfard, and M. Reza Meybodi Efficient stochastic algorithms for document clustering Inform. Sci. 220 2013 269 291
16. R.M. French When coffee cups are like old elephants or why representation modules don't make sense A. Riegler, M. Peschl, Proc. of the 1997 International Conference on New Trends in Cognitive Science 1997 Austrian Society for Cognitive Science 158 163
17. R.M. French, and C. Labiouse Why co-occurrence information alone is not sufficient to answer subcognitive questions J. Exp. Theor. Artif. Intell. 13 2001 421 429
18. R.M. French, and C. Labiouse Four problems with extracting human semantics from large text corpora Proc. of the 24th Annual Conference of the Cognitive Science Society 2002 George Mason University 316 321
19. B.J. Frey, and D. Dueck Clustering by passing messages between data points Science 315 2007 972 976
20. M. Hollander, and D.A. Wolfe Nonparametric Statistical Methods 1973 Wiley
21. A. Hotho, S. Staab, and G. Stumme Ontologies improve text document clustering Proc. of the Third IEEE International Conference on Data Mining (ICDM-2003) 2003 IEEE Computer Society Washington, DC, USA 541 544
22. X. Hu, R. Eberhart, and Y. Shi Swarm intelligence for permutation optimization: a case study on n-queens problem Proc. of the IEEE Swarm Intelligence Symposium 2003 IEEE Press 243 246
23. X. Hu, N. Sun, C. Zhang, and T. Chua Exploiting internal and external semantics for the clustering of short texts using world knowledge Proc. of the 18th ACM Conf. on Information and Knowledge Management 2009 ACM New York, NY, USA 919 928
24. D. Ingaramo, M. Errecalde, L. Cagnina, and P. Rosso Computational intelligence and bioengineering F. Masulli, A. Micheli, A. Sperduti, Computational Intelligence and Bioengineering 2009 IOS Press 3 19
25. D. Ingaramo, M. Errecalde, and P. Rosso A general bio-inspired method to improve the short-text clustering task Proc. of 11th Int. Conf. on Intelligent Text Processing and Computational Linguistics, CICLing 2010 Lecture Notes in Computer Science vol. 6008 2010 Springer Berlin/Heidelberg 661 672
26. D. Ingaramo, D. Pinto, P. Rosso, and M. Errecalde Evaluation of internal validity measures in short-text corpora Proc. of the Int. Conf. on Intelligent Text Processing and Computational Linguistics, CICLing 2008 Lecture Notes in Computer Science vol. 4919 2008 Springer-Verlag 555 567
27. A.K. Jain, and R.C. Dubes Algorithms for Clustering Data 1988 Prentice Hall
28. T. Karthikeyan, S.J. Peter, and S. Chidambaranathan Hybrid algorithm for noise-free high density clusters with self-detection of best number of clusters Int. J. Hybrid Inform. Technol. 4 2011 39 54
29. G. Karypis, E. Han, and V. Kumar Chameleon: hierarchical clustering using dynamic modeling Computer 32 1999 68 75
30. T. Krink, and S. Paterlini Differential evolution and particle swarm optimisation in partitional clustering Comput. Stat. Data Anal. 50 2006 1220 1247
31. N. Labroche, N. Monmarché, and G. Venturini Antclust: ant clustering and web usage mining Genetic and Evolutionary Computation GECCO 2003 Lecture Notes in Computer Science vol. 2723 2003 Springer Berlin Heidelberg 25 36
32. A. Linhares, and D.M. Chada What is the nature of the mind's pattern-recognition process? New Ideas Psychol. 31 2013 108 121
33. J.B. MacQueen Some methods for classification and analysis of multivariate observations Proc. of 5th Berkeley Symposium on Mathematical Statistics and Probability 1967 University of California Press 281 297
34. P. Makagonov, M. Alexandrov, and A. Gelbukh Clustering abstracts instead of full texts Proc. of Int. Conf. on Text, Speech and Dialogue, TSD 2004 Lecture Notes in Artificial Intelligence vol. 3206 2004 Springer-Verlag 129 135
35. C.D. Manning, P. Raghavan, and H. Schutze Introduction to Information Retrieval 2008 Cambridge University Press
36. C.D. Manning, and H. Schütze Foundations of Statistical Natural Language Processing 1999 The MIT Press Cambridge, MA, USA
37. D. Pinto, J.M. Benedí, and P. Rosso Clustering narrow-domain short texts by using the Kullback-Leibler distance Proc. of the Int. Conf. on Intelligent Text Processing and Computational Linguistics, CICLing 2007 Lecture Notes in Computer Science vol. 4394 2007 Springer Verlag 611 622
38. D. Pinto, and P. Rosso On the relative hardness of clustering corpora Proc. of Int. Conf. on Text, Speech and Dialogue, TSD 2007 Lecture Notes in Artificial Intelligence vol. 4629 2007 Springer-Verlag 155 161
39. M. Sahami, and T.D. Heilman A web-based kernel function for measuring the similarity of short text snippets Proc. of the 15th International Conference on World Wide Web (WWW-2006) 2006 ACM New York, NY, USA 377 386
40. F. Samadzadegan, S. Saeedi, Clustering of lidar data using particle swarm optimization algorithm in urban area, in: Laserscanning 09, vol. XXXVIII, 2009, pp. 334-339.
41. Y. Shi, and R. Eberhart A modified particle swarm optimizer Proc. of the IEEE Int. Conf. on Evolutionary Computation 1998 IEEE Press 69 73
42. P. Shrestha, C. Jacquin, and B. Daille Clustering short text and its evaluation A. Gelbukh, Computational Linguistics and Intelligent Text Processing Lecture Notes in Computer Science vol. 7182 2012 Springer Berlin/Heidelberg 169 180
43. B. Stein, and S. Meyer zu Eissen Document categorization with MajorClust Proc. Workshop on Information Technologies and Systems, WITS 2002 2002 Technical University of Barcelona 91 96
44. B. Stein, S. Meyer zu Eissen, and F. Wissbrock On cluster validity and the information need of users Proc. of the Int. Association of Science and Technology for Development, IASTED 2003 2003 ACTA Press 216 221
45. R. Steinberger, B. Pouliquen, A. Widiger, C. Ignat, T. Erjavec, D. Tufis, and D. Varga The JRC-Acquis: a multilingual aligned parallel corpus with 20+ languages Proc. of the 5th Int. Conf. on Language Resources and Evaluation, LREC 2006 2006 European Language Resources Association 2142 2147
46. P. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison Wesley, 2006.
47. J.W. Tukey Exploratory Data Analysis 1977 Addison-Wesley Publishing Company Reading, MA
48. P.D. Turney Mining the web for synonyms: Pmi-ir versus lsa on toefl Proc. of the 12th European Conference on Machine Learning, EMCL '01 2001 Springer-Verlag London, UK 491 502
49. D.W. van der Merwe, and A.P. Engelbrecht Data clustering using particle swarm optimization Proc. of the Congress on Evolutionary Computation Special Session on Design Optimisation with Evolutionary Computation, CEC 2003 2003 IEEE Press 215 220
50. X. Xiao, E. Dow, R. Eberhart, Z.B. Miled, and R. Oppelt Gene clustering using self-organizing maps and particle swarm optimization Proc. of the 17th Int. Symposium on Parallel and Distributed Processing 2003 IEEE Computer Society
51. W.T. Yih Learning term-weighting functions for similarity measures Proc. of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP '09 2009 Association for Computational Linguistics Stroudsburg, PA, USA 793 802
52. W.T. Yih, and C. Meek Improving similarity measures for short segments of text Proc. of the 22nd National Conference on Artificial Intelligence (AAAI-2007) 2007 AAAI Press 1489 1494
53. Y. Zhao, and G. Karypis Empirical and theoretical comparison of selected criterion functions for document clustering Mach. Learn. 55 2004 311 331