K-AP: Generating specified K clusters by efficient Affinity Propagation

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 1187-1192

  Zhang, Xiangliang ^a   Wang, Wei ^b   Nørvåg, Kjetil ^c   Sebag, Michèle ^d  

^a KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

^b UNIVERSITY OF LUXEMBOURG   (Luxembourg)

^c NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^d INRIA   (France)

Author keywords

Affinity Propagation; Clustering; K medoids

Indexed keywords

COST BENEFIT ANALYSIS; DATA MINING; MESSAGE PASSING;

AFFINITY PROPAGATION; CLUSTERING; CLUSTERING QUALITY; COMPUTATIONAL COSTS; DISTORTION MINIMIZATION; EXPERIMENTAL VALIDATIONS; K-MEDOIDS; USER-DEFINED PARAMETERS;

CLUSTERING ALGORITHMS;

EID: 79951743719     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.107     Document Type: Conference Paper

References (14)

1. C. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
2. Y. Weiss C. Yanover, T. Meltzer. Linear programming relaxations and belief propagation - an empirical study. Journal of Machine Learning Research, 7:1887-1907, 2006. (Pubitemid 44423552)
3. D. Dueck. Affinity Propagation: Clustering Data by Passing Messages. PhD thesis, University of Toronto, 2009.
4. A. Frank and A. Asuncion. UCI machine learning repository, 2010.
5. B. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315:972-976, 2007. (Pubitemid 46281181)
6. L. Kaufman and P. Rousseeuw. Clustering by means of medoids. In Statistical Data Analysis Based on the L1 Norm and Related Methods, pages 405-416. 1987.
7. L. Kaufman and P.J. Rousseeuw. Finding Groups in Data: an introduction to cluster analysis. Wiley, 1990.
8. F. Kschischang, B. Frey, and H. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47:498-519, 2001. (Pubitemid 32318087)
9. H. Loeliger. An introduction to factor graphs. IEEE Signal Processing Magazine, pages 28-41, 2004.
10. M. Mezard. Computer science: Where are the exemplars? Science, 315:949-951, 2007. (Pubitemid 46281175)
11. R. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In VLDB, pages 144-155, 1994.
12. M. Rege, M. Dong, and F. Fotouhi. Co-clustering documents and words using bipartite isoperimetric graph partitioning. In ICDM, pages 532-541, 2006. (Pubitemid 47485832)
13. T. Yang, R. Jin, Y. Chi, and S. Zhu. Combining link and content for community detection: a discriminative approach. In SIGKDD, pages 927-936, 2009.
14. J. Yedidia, W. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. Exploring artificial intelligence in the new millennium, pages 239-269, 2003.