Optimal meta search results clustering

SIGIR 2010 Proceedings - 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

Volumn , Issue , 2010, Pages 170-177

  Carpineto, Claudio ^a   Romano, Giovanni ^a  

^a FONDAZIONE UGO BORDONI   (Italy)

Author keywords

Meta clustering; Optimization; Search results clustering

Indexed keywords

CLUSTERING COMBINATION; CLUSTERING PROBLEMS; CLUSTERINGS; DOCUMENTS RANKING; EVALUATION TECHNIQUES; META CLUSTERING; META SEARCH; MULTIPLE SEARCH; OBJECTIVE FUNCTIONS; OPTIMAL VALUES; OPTIMIZATION PROBLEMS; PERFORMANCE IMPROVEMENTS; RELEVANCE JUDGMENT; SEARCH RESULTS; SEARCH RESULTS CLUSTERING; SIMPLE STOCHASTIC; SINGLE OUTPUT; TEST COLLECTION; UNIQUE FEATURES; UPPER BOUND;

APPROXIMATION ALGORITHMS; INFORMATION RETRIEVAL; INFORMATION RETRIEVAL SYSTEMS; OPTIMIZATION;

CLUSTERING ALGORITHMS;

EID: 77956044076     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835449.1835480     Document Type: Conference Paper

References (18)

1. A. Bernardini, C. Carpineto, and M. D'Amico. Full-Subtopic Retrieval with Keyphrase-Based Search Results Clustering. In Proc. Web Intelligence 2009, Milan, Italy, pages 206-213. IEEE Computer Society, 2009.
2. R. J. G. B. Campello. A fuzzy extension of the Rand index and other related indexes for clustering and classification assessment. Pattern Recognition Letters, 28(7):833-841, 2007.
3. C. Carpineto, S. Mizzaro, G. Romano, and M. Snidero. Mobile Information Retrieval with Search Results Clustering: Prototypes and Evaluations. JASIST, 60(5):877-895, 2009.
4. C. Carpineto, S. Osiński, G. Romano, and D. Weiss. A survey of Web clustering engines. ACM Computing Survey, 41(3), 2009.
5. G. L. Liu. Introduction to Combinatorial Mathematics. McGraw Hill, 1968.
6. A. Fred and A. Jain. Data clustering using evidence accumulation. In ICPR, pages 276-280, 2002.
7. A. Goder and V. Filkov. Consensus Clustering Algorithms: Comparison and Refinement. In Proc. ALENEX 2008, San Francisco, CA, USA, pages 109-117, 2008.
8. L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2(1):193-218, 1985.
9. S. Luke. Essentials of Metaheuristics. 2009. available at http://cs.gmu.edu/~sean/book/metaheuristics/.
10. C. D. Manning, P. Raghavan, and H. Schütze. Introduction to Information Retrieval. Cambridge University Press, 2008.
11. S. Osiński and D. Weiss. A Concept-Driven Algorithm for Clustering Search Results. IEEE Intelligent Systems, 20(3):48-54, 2005.
12. M. S. P.-N. Tan and V. Kumar. Introduction to Data Mining, chapter 8: Cluster analysis: basic concepts and algorithms, pages 487-568. Addison Wesley, 2005.
13. W. M. Rand. Objective criteria for the evaluation of clustering methods. Journal of American Statistical Association, 66:846-850, 1971.
14. A. Strehl and J. Ghosh. Cluster Ensembles - A Knowledge Reuse Framework for Combining Multiple Partitions. JMLR, (3):583-617, 2002.
15. A. Topchy, A. K. Jain, and W. Punch. Clustering ensembles: models of consensus and weak partitions. IEEE Trans. PAMI, 27(12):1866-1881, 2005.
16. K. van Rijsbergen. Information Retrieval. Butterworth-Heinemann, 1979.
17. Y. Wakabayashi. The Complexity of Computing Medians of Relations. Resenhas, 3(3):323-349, 1998.
18. H. Wang, H. Shan, and A. Banerjee. Bayesian Cluster Ensembles. In Proc. SDM 2009, pages 209-220, 2009.