A document clustering algorithm for discovering and describing topics

Pattern Recognition Letters

Volumn 31, Issue 6, 2010, Pages 502-510

  Anaya Sánchez, Henry ^b   Pons Porrata, Aurora ^a   Berlanga Llavori, Rafael ^b  

^a UNIVERSIDAD DE ORIENTE   (Cuba)

^b UNIVERSITAT JAUME I   (Spain)

Author keywords

Document clustering; Topic description; Topic discovery

Indexed keywords

DOCUMENT CLUSTERING; NEW APPROACHES; TEXT COLLECTION; TOPIC DISCOVERY;

CLUSTER ANALYSIS; INFORMATION RETRIEVAL;

CLUSTERING ALGORITHMS;

EID: 77249115630     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.11.013     Document Type: Article

References (16)

1. Anaya-Sánchez, H., Pons-Porrata, A., Berlanga-Llavori, R., 2008. A new document clustering algorithm for topic discovering and labeling. In: Proceedings of the CIARP'08, Lecture Notes in Computer Science, vol. 5197. Springer, pp. 161-168.
2. Berlanga-Llavori, R., Anaya-Sánchez, H., Pons-Porrata, A., Jimenez-Ruiz, E., 2008. Conceptual subtopic identification in the medical domain. In: Advances in Artificial Intelligence - IBERAMIA 2008, Lecture Notes in Computer Science, vol. 5197. Springer, pp. 312-321.
3. Buckley, C., Salton, G., Allan, J., Singhal, A., 1995. Automatic query expansion using smart: Trec-3. In: Proceedings of the Third TREC Conference, pp. 69-80.
4. Cutting, D.R., Pedersen, J.O., Karger, D.R., Tukey, J.W., 1992. Scatter/gather: A cluster-based approach to browsing large document collections. In: Belkin, N.J., Ingwersen, P., Pejtersen, A.M. (Eds.), Proceedings of the 15th Annual Internat. ACM SIGIR Conf. on Research and Development in Information Retrieval. ACM, pp. 318-329.
5. Duda R., Hart P., and Stork D. Pattern Classification (2001), Wiley, New York
6. Dunning T. Accurate methods for the statistics of surprise and coincidence. Comput. Linguistics 19 1 (1993) 61-74
7. Fung, B., Wang, K., Martin, E., 2003. Hierarchical document clustering using frequent itemsets. In: Barbará, D., Kamath, C. (Eds.), Proceedings of the Third SIAM Internat. Conf. on Data Mining. pp. 59-70.
8. Harabagiu, S., Lacatusu, V., 2005. Topic themes for multi-document summarization. In: Proceedings of the 28th Annual Internat. ACM SIGIR Conf. on Research and Development in Information Retrieval. pp. 202-209.
9. Li Y., Chung S., and Holt J. Text document clustering based on frequent word meaning sequences. Data Knowledge Eng. 64 1 (2008) 381-404
10. Lin, C.-Y., Hovy, E., 2000. The automated acquisition of topic signatures for text summarization. In: 18th Internat. Conf. on Computational Linguistics, COLING 2000. pp. 495-501.
11. Loftsgaarden D., and Quesenberry C. A nonparametric estimate of a multivariate density function. Ann. Math. Statist. 36 3 (1965) 1049-1051
12. Malik, H., Kender, J., 2006. High quality, efficient hierarchical document clustering using closed interesting itemsets. In: Proceedings of the ICDM 2006. IEEE Computer Society, pp. 991-996.
13. Pons-Porrata A., Berlanga-Llavori R., and Ruiz-Shulcloper J. Topic discovery based on text mining techniques. J. Inform. Process. Manage. 43 3 (2007) 752-768
14. Sibson R. Slink: An optimally efficient algorithm for the single-link cluster method. Comput. J. 16 1 (1973) 30-34
15. Zamir O., Etzioni O., Madani O., and Karp R. Fast and intuitive clustering of web documents. In: Heckerman D., Mannila H., and Pregibon D. (Eds). Proceedings of the Third Internat. Conf. on Knowledge Discovery and Data Mining (KDD-97) (1997), AAAI Press 287-290
16. Zhang C., Zhang X., Zhang M., and Li Y. Neighbor number, valley seeking and clustering. Pattern Recognition Lett. 28 2 (2007) 173-180