An efficient LSI based information retrieval framework usingp particle swarm optimization and simulated annealing approach

Proceedings of the 2008 16th International Conference on Advanced Computing and Communications, ADCOM 2008

Volumn , Issue , 2008, Pages 94-101

  Latha, K ^a   Rajaram, R ^a  

^a THIAGARAJAR COLLEGE OF ENGINEERING   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; INFORMATION RETRIEVAL; INFORMATION SERVICES; INFORMATION THEORY; PARTICLE SWARM OPTIMIZATION (PSO); SEARCH ENGINES; WORLD WIDE WEB;

AMOUNT OF INFORMATIONS; K-MEANS ALGORITHMS; KEY FACTORS; LATENT SEMANTIC INDEXING; PARTICLE SWARMS; PARTICLE-SWARM OPTIMIZATIONS; RELEVANT DOCUMENTS; SPECIFIC INFORMATIONS; TEXT MININGS; TEXT PREPROCESSINGS; WEB SEARCH ENGINES; WEB USERS;

SIMULATED ANNEALING;

EID: 63149139122     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ADCOM.2008.4760433     Document Type: Conference Paper

References (17)

1. The TREC, co-sponsored by the NIST, Information Technology Laboratory's (ITL) Retrieval Group of the Information Access Division (IAD) and the Advanced Research and Development Activity (ARDA) of the U. S. Department of Defense. http://trec.nist.gov,2006.
2. PubMedCentralOpenAccessInitiative,http://www.pubmedcentral.nih.gov/about/ openftplist.html,2004.
3. 7-Sectors. WebKb dataset Providers. http://www.webkb.org.
4. C. Scott Deerwester, T. Susan Dumais, W. George Furnas, K. Thomas Landauer and A. Richard Harshman."Indexing by Latent Semantic Analysis", Journal of the American Society of Information Science, 1990, 41(6): 391-407
5. Thomas Hofmann."Probabilistic Latent Semantic Indexing", Proceedings of the Twenty-Second Annual International SIGIR Conference on Research and Development in Information Retrieval, International Computer Science Institute, Berkeley, CA & EECS Department, CS Division, UC Berkeley,1999.
6. B. Tang and X. Luo,"Heywood M.I. Shepherd M."A Comparative Study of Dimension Reduction Techniques for Document Clustering" ,TR # CS-2004-14, Faculty of Computer Science, Dalhousie University, 2004. http://www.cs.dal.ca/research/techreports/2004/CS-2004-14.shtml
7. Y. Zhao and G. Karypis,"Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering", Machine Learning, 2004, 55(3): pp. 311-331.
8. J. Kennedy, R. C. Eberhart Y. Shi,Swarm Intelligence, Morgan Kaufmann Publishers, San Francisco,2001.
9. Kennedy, J. and Eberhart, R,"Particle Swarm Optimization", Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942-1948, IEEE Press.
10. S. Z. Selim, and M. A. Ismail,"K-means type algorithms: A generalized convergence theorem and characterization of local optimality". IEEE Trans. Pattern Anal. 1984, Mach. Intell. 6, 81-87.
11. Xiaohui Cui, E. Thomas Potok,"Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm", Journal of Computer Sciences(Special Issue): 27-33, 2005, ISSN 1549-3636, Science Publications.
12. S. Kirkpatrick; C. D. Gelatt; M. P. Vecchi," Optimization by Simulated Annealing", Science, New Series, Vol. 220, No. 4598, (May 13, 1983) pp. 671-680.
13. Jin-kao Hao and Jerome Pannier,"Simulated Annealing and Tabu Search for Constraint Solving", Fifth Intl. Symposium on Artificial Intelligence and Mathematics Fort Lauderdale, Florida, USA, (AIM'98).
14. M. R. Anderberg,Cluster Analysis for Applications. Academic Press, Inc., New York, 1973.
15. Markus Koskela., Jorma Laaksonen., and Erkki Oja,"Entropy-based measures for clustering and SOM topology preservation applied to content-based image indexing and retrieval," Proceedings of the 17th International Conference on PatternRecognition (ICPR'04), IEEE computer society,2004, 1051-4651.
16. Pang-Ning Tan., Michael Steinbach. and Vipin Kumar,Introduction to Data Mining-Book,Chapter 8-Cluster Analysis:Basic Concepts and Algorithms, First Edition, Pearson-Addison Wesley Higher Education publishers,2005, pages:532-568.
17. Jesse Davis and Mark Goadrich,"The Relationship Between Precision-Recall and ROC Curves", Proceedings of the 23rdInternational Conference on Machine Learning, Pittsburgh, 2006.