On-line evolutionary exponential family mixture

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2009, Pages 1610-1615

  Zhang, Jianwen ^a   Song, Yangqiu ^a   Chen, Gang ^a   Zhang, Changshui ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MIXTURES;

CLUSTERING PROBLEMS; CONVENTIONAL CLUSTERING; CURRENT MODELING; DENSITY ESTIMATION; EVOLUTIONARY CLUSTERING; EVOLUTIONARY SETTINGS; EXPONENTIAL FAMILY; HISTORICAL MODELS;

CLUSTERING ALGORITHMS;

EID: 78751698489     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. A. Ahmed and E. Xing. Dynamic non-parametric mixture models and the recurrent Chinese restaurant process: with applications to evolutionary clustering. SDM, 2008.
2. Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon, and Joydeep Ghosh. Clustering with Bregman divergences. JMLR, 6:1705-1749, 2005.
3. M.J. Beal. Variational Algorithms for Approximate Bayesian Inference. Thesis for degree of doctor of philosophy, University of London, 2003.
4. D. Chakrabarti, R. Kumar, and A. Tomkins. Evolutionary clustering. KDD, 2006.
5. M. Charikar, C. Chekuri, T. Feder, and R. Motwani. Incremental clustering and dynamic information retrieval. ACM Symposium on Theory of Computing, 1997.
6. Y. Chi, X.-D. Song, D.-Y. Zhou, K. Hino, and B. L. Tseng. Evolutionary spectral clustering by incorporating temporal smoothness. KDD, 2007.
7. A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and A. J. Smola. A kernel approach to comparing distributions. AAAI, 2007.
8. J. A. Hartigan and M. A. Wong. A k-means clustering algorithm. Applied Statistics, 28(1):100-108, 1979.
9. A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. NIPS, 2002.
10. Y. Rubner, C. Tomasi, and LJ Guibas. A metric for distributions with applications to image databases. ICCV, 1998.
11. L. Tang, H. Liu, J.-P. Zhang, and Z. Nazeri. Community evolution in dynamic multi-mode networks. KDD, 2008.
12. V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000.
13. Y. Wang, S.-X. Liu, L.-Z. Zhou, and H. Su. Mining naturally smooth evolution of clusters from dynamic data. SDM, 2007.