Sat, 26 Jul 2008 07:44:07 BST CiteULike: mshafiei's clustering http://www.citeulike.org/user/mshafiei/tag/clustering CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/mshafiei/article/874146 <i>(2006), pp. 65-74.</i> Bibliometric impact measures leveraging topic analysis Gideon Mann David Mimno Andrew Mccallum doi:10.1145/1141753.1141765 (2006), pp. 65-74. 2006-09-26T18:30:44-00:00 2006 65 74 ACM Press bibliometrics clustering digital_libraries impact measures topic http://www.citeulike.org/user/mshafiei/article/607999 <i>(2001), pp. 25-32.</i> Bipartite graph partitioning and data clustering Hongyuan Zha Xiaofeng He Chris Ding Horst Simon Ming Gu doi:10.1145/502585.502591 (2001), pp. 25-32. 2006-04-30T15:04:00-00:00 2001 25 32 ACM Press clustering spectral http://www.citeulike.org/user/mshafiei/article/2647146 <i>(2007), pp. 1159-1166.</i> Spectral clustering and transductive learning with multiple views Dengyong Zhou Christopher Burges doi:10.1145/1273496.1273642 (2007), pp. 1159-1166. 2008-04-09T19:37:58-00:00 2007 1159 1166 ACM clustering graphs http://www.citeulike.org/user/mshafiei/article/2594275 <i>Structural, Syntactic, and Statistical Pattern Recognition (2004), pp. 662-670.</i><br /><br />Several clustering algorithms equipped with pairwise hard constraints between data points are known to improve the accuracy of clustering solutions. We develop a new clustering algorithm that extends mixture clustering in the presence of (i) soft constraints, and (ii) group-level constraints. Soft constraints can reflect the uncertainty associated with a priori knowledge about pairs of points that should or should not belong to the same cluster, while group-level constraints can capture larger building blocks of the target partition when afforded by the side information. Assuming that the data points are generated by a mixture of Gaussians, we derive the EM algorithm to estimate the parameters of different clusters. Empirical study demonstrates that the use of soft constraints results in superior data partitions normally unattainable without constraints. Further, the solutions are more robust when the hard constraints may be incorrect. Clustering with Soft and Group Constraints Martin Law Alexander Topchy Anil Jain Structural, Syntactic, and Statistical Pattern Recognition (2004), pp. 662-670. 2008-03-26T14:07:31-00:00 2004 Structural, Syntactic, and Statistical Pattern Recognition 662 670 clustering http://www.citeulike.org/user/mshafiei/article/2453881 <i>(2008)</i> A Segment-based Approach To Clustering Multi-Topic Documents Andrea Tagarelli George Karypis (2008) 2008-03-01T22:10:31-00:00 2008 clustering segmentation topic_detection http://www.citeulike.org/user/mshafiei/article/2570774 <i>pp. 317-327.</i><br /><br />In analyzing data from multiple related studies, it often is of interest to borrow information across studies and to cluster similar studies. Although parametric hierarchical models are commonly used, of concern is sensitivity to the form chosen for the random-effects distribution. A Dirichlet process (DP) prior can allow the distribution to be unknown, while clustering studies; however, the DP does not allow local clustering of studies with respect to a subset of the coefficients without making independence assumptions. Motivated by this problem, we propose a matrix stick-breaking process (MSBP) as a prior for a matrix of random probability measures. Properties of the MSBP are considered, and methods are developed for posterior computation using Markov chain Monte Carlo. Using the MSBP as a prior for a matrix of study-specific regression coefficients, we demonstrate advantages over parametric modeling in simulated examples. The methods are further illustrated using a multinational uterotrophic bioassay study. The Matrix Stick-Breaking Process: Flexible Bayes Meta-Analysis David Dunson pp. 317-327. 2008-03-21T22:39:53-00:00 317 327 bayesian clustering dependent-partition dirichlet-process hierarchical matrix mixture-model nonparametric prior http://www.citeulike.org/user/mshafiei/article/2517325 <i>J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577.</i> A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior Hal Iii Daniel Marcu J. Mach. Learn. Res., Vol. 6 (2005), pp. 1551-1577. 2008-03-12T01:37:16-00:00 2005 J. Mach. Learn. Res. 1533-7928 6 1551 1577 MIT Press bayesian clustering conjugate crp mcmc non-conjugate nonparametric prior sampling supervised http://www.citeulike.org/user/mshafiei/article/2458674 <i>(2005), pp. 532-537.</i> Model-based overlapping clustering Arindam Banerjee Chase Krumpelman Joydeep Ghosh Sugato Basu Raymond Mooney doi:10.1145/1081870.1081932 (2005), pp. 532-537. 2008-03-02T23:01:46-00:00 2005 532 537 ACM clustering overlapping http://www.citeulike.org/user/mshafiei/article/1239722 <i>(2005), pp. 297-304.</i> Bayesian hierarchical clustering Katherine Heller Zoubin Ghahramani doi:10.1145/1102351.1102389 (2005), pp. 297-304. 2007-04-20T13:27:16-00:00 2005 297 304 ACM Press bayesian clustering dirichlet hierarchical mixture models process http://www.citeulike.org/user/mshafiei/article/2304494 <i>(2007), pp. 470-479.</i> A probabilistic framework for relational clustering Bo Long Zhongfei Zhang Philip Yu doi:10.1145/1281192.1281244 (2007), pp. 470-479. 2008-01-29T16:35:34-00:00 2007 470 479 ACM clustering network-analysis http://www.citeulike.org/user/mshafiei/article/1983303 <i>(2007)</i> A Nonparametric Bayesian Approach to Modeling Overlapping Clusters KA Heller Z Ghahramani (2007) 2007-11-26T03:17:47-00:00 2007 bayesian clustering non-parametric http://www.citeulike.org/user/mshafiei/article/1952481 <i>(2004)</i> Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes YW Teh MI Jordan MJ Beal DM Blei (2004) 2007-11-21T15:18:40-00:00 2004 bayesian clustering dirichlet non-parametric http://www.citeulike.org/user/mshafiei/article/965794 <i>Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581.</i> Hierarchical Dirichlet Processes Teh Yee Whye Jordan I Michael Beal J Matthew Blei M David doi:10.1198/016214506000000302 Journal of the American Statistical Association, Vol. 101, No. 476. (December 2006), pp. 1566-1581. 2006-11-29T01:37:01-00:00 2006 Journal of the American Statistical Association 0162-1459 101 476 1566 1581 American Statistical Association bayesian clustering dirichlet nonparametric