Evolutionary clustering

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn 2006, Issue , 2006, Pages 554-560

  Chakrabarti, Deepayan ^a   Kumar, Ravi ^a   Tomkins, Andrew ^a  

^a YAHOO RESEARCH   (United States)

Author keywords

Agglomerative; Clustering; k means; Temporal Evolution

Indexed keywords

DATABASE SYSTEMS; EVOLUTIONARY ALGORITHMS; HIERARCHICAL SYSTEMS; OPTIMIZATION; PROBLEM SOLVING;

CLUSTERING ALGORITHMS; EVOLUTIONARY CLUSTERING; HIERARCHICAL CLUSTERING; TEMPORAL EVOLUTION;

DATA MINING;

EID: 33749572698     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1150402.1150467     Document Type: Conference Paper

References (18)

1. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proceedings of the International Conference on Very Large Data Bases, pages 852-863, 2003.
2. J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang. Topic detection and tracking pilot study: Final report. Proc. of the DARPA Broadcast News Transcription and Understanding Workshop, 1998.
3. P. Auer and M. Warmuth. Tracking the best disjunction. In Proceedings of the 36th Annual Symposium on Foundations of Computer Science, pages 312-321, 1995.
4. D. Blei, T. Griffiths, M. Jordan, and J. Tenenbaum. Hierarchical topic models and the nested Chinese restaurant process. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16, 2004.
5. C. Chatfield. The Analysis of Time Series. Chapman and Hall, 1984.
6. S. Chien and N. Immorlica. Semantic similarity between search engine queries using temporal correlation. In Proceedings of the International Conference on the World- Wide Web, pages 2-11, 2005.
7. I. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42:143-175, 2001.
8. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley-Interscience, 2000.
9. D. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139-172, 1987.
10. S. Guha, N. Mishra, R. Motwani, and L. O'Callaghan. Clustering data streams. In IEEE Symposium on Foundations of Computer Science, pages 359-366, 2000.
11. M. Herbster and M. K. Warmuth. Tracking the best linear predictor. Journal of Machine Learning Research, 1:281-309, 2001.
12. J. Lin, M. Vlachos, E. Keogh, and D. Gunopulos. Iterative incremental clustering of time series. In Proceedings of the International Conference on Extending Database Technology, pages 106-122, 2004.
13. M. Meila. Comparing clusterings by the variation of information. In Proceedings of the ACM Conference on Computational Learning Theory, pages 173-187, 2003.
14. P. Smyth. Clustering sequences with hidden Markov models. In Advances in Neural Information Processing Systems, volume 9, page 648, 1997.
15. M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 131-142, 2004.
16. I. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
17. Y. Yang, T. Pierce, and J. Carbonell. A study on retrospective and on-line event detection. In Proceedings of the 21st ACM International Conference on Research and Development in Information Retrieval, pages 28-36, 1998.
18. J. Zhang, Z. Ghahramani, and Y. Yang. A probabilistic model for online document clustering with applications to novelty detection. In Proceedings of Advances in Neural Information Processing Systems, 2005.