Clustering with Instance and Attribute Level Side Information

References

• Jain , A. K. , Murty , M. N. and Flynn , P. J. 1999 . Data clustering: a review . ACM Computing Surveys , 31 ( 3 ) : 264 – 323 .
   Web of Science ®Google Scholar
• Brouwer , R. K. 2008 . Clustering feature vectors with mixed numerical and categorical attributes . International Journal of Computational Intelligence Systems , 1 ( 4 ) : 285 – 298 .
   Web of Science ®Google Scholar
• Brouwer , R. K. 2009 . Fuzzy relational fixed point clustering . International Journal of Computational Intelligence Systems , 2 ( 1 ) : 69 – 82 .
   Web of Science ®Google Scholar
• Ilhan , S. , Duru , N. and Adali , E. 2010 . Improved fuzzy art method for initializing k-means . International Journal of Computational Intelligence Systems , 3 ( 3 ) : 274 – 279 .
   Web of Science ®Google Scholar
• Basu , S. , Bilenko , M. and Mooney , R. J. A probabilistic framework for semi-supervised clustering . Proc. of the 10th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining . pp. 59 – 68 .
   Google Scholar
• Grira , N. , Crucianu , M. and Boujema , N. 2005 . “ Unsupervised and semi-supervised clustering: a brief survey ” . In a Review of Machine Learning Techniques for Processing Multimedia Content, Report of the MUSCLE European Network of Excellence FP6
   Google Scholar
• Davidson , I. , K. Wagstaff and Basu , S. Measuring constraint-set utility for partitional clustering algorithms . Proc. of the 10th Euro. Conf. on Principle and Practice of Knowledge Discovery in Databases . pp. 115 – 126 .
   Google Scholar
• Yang , L. and Jin , R. 2006 . Distance metric learning: A comprehensive survey , Michigan State Universiy .
   Google Scholar
• Kulis , R. , Basu , S. , Dhillon , I. and Mooney , R. 2009 . Semisupervised graph clustering: a kernel approach . Mach. Learn. , 74 : 1 – 22 .
   Web of Science ®Google Scholar
• Yin , X. S. , Chen , S. C. , Hu , E. L. and Zhang , D. Q. 2010 . Semi-supervised clustering with metric learning: an adaptive kernel method . Pattern Recognition , 43 ( 4 ) : 1320 – 1333 .
   Web of Science ®Google Scholar
• K. Wagstaff and Cardie , C. Clustering with instancelevel constraints . Proc. of the 17th Intl. Conf. on Machine Learning . pp. 1103 – 1110 .
   Google Scholar
• K. Wagstaff , Cardie , C. , Rogers , S. and Schrödl , S. Constrained k-means clustering with background knowledge . Proc. of the 18th Intl. Conf. on Machine Learning . pp. 577 – 584 .
   Google Scholar
• Klein , D. , Kamvar , S. D. and Manning , C. D. From instance-level constraints to space-level constraints: Making the most of prior knowledge in data clustering . Proc. of the 19th Intl. Conf. on Machine Learning . pp. 307 – 314 .
   Google Scholar
• Shental , N. , Bar-hillel , A. and Weinshall , D. 2003 . “ Computing gaussian mixture models with em using equivalence constraints ” . In Advances in Neural Information Processing Systems Vol. 16 ,
   Google Scholar
• Bilenko , M. , Basu , S. and Mooney , R. J. Integrating constraints and metric learning in semi-supervised clustering . Proc. of the 21st Intl. Conf. on Machine Learning . pp. 81 – 88 .
   Google Scholar
• Kumar , N. and Kummamuru , K. 2008 . Semi-supervised clustering with metric learning using relative comparisons . IEEE Transactions on Knowledge and Data Engineering , 20 ( 4 ) : 496 – 503 .
   Web of Science ®Google Scholar
• Xing , E. P. , Ng , A. Y. , Jordan , M. I. and Russell , S. J. 2002 . Distance metric learning with application to clustering with side-information . Advances in Neural Information Processing Systems , 15 : 505 – 512 .
   Google Scholar
• Bar-Hillel , A. , Hertz , T. , Shental , N. and Weinshall , D. 2005 . Learning a mahalanobis metric from equivalence constraints . J. Mach. Learn. Res. , 6 : 937 – 965 .
   Web of Science ®Google Scholar
• Halkidi , M. , Gunopulos , D. , Vazirgiannis , M. , Kumar , N. and Domeniconi , C. 2008 . A clustering framework based on subjective and objective validity criteria . ACM Trans. Knowl. Discov. Data , 1 ( 4 ) : 1 – 25 .
   Google Scholar
• Xiang , S. , Nie , F. and Zhang , C. 2008 . Learning a Mahalanobis distance metric for data clustering and classification . Pattern Recognition , 41 ( 12 ) : 3600 – 3612 .
   Web of Science ®Google Scholar
• Basu , S. , Banerjee , A. and Mooney , R. J. Active semi-supervision for pairwise constrained clustering . Proc. of the 4th SIAM Intl. Conf. on Data Mining . pp. 333 – 344 .
   Google Scholar
• Huang , A. , Milne , D. , Frank , E. and Witten , I. H. Clustering documents with active learning using Wikipedia . Proc. of the 8th IEEE Intl. Conf. on Data Mining . pp. 839 – 844 .
   Google Scholar
• Huang , R. and Lam , W. 2009 . An active learning framework for semi-supervised document clustering with language modeling . Data & Knowledge Engineering , 68 ( 1 ) : 49 – 67 .
   Web of Science ®Google Scholar
• Wang , J. , Wu , S. , H , Vu. and Li , G. Text document clustering with metric learning . Proc. of the 33rd Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval . pp. 783 – 784 .
   Google Scholar
• Banerjee , S. , Ramanathan , K. and Gupta , A. Clustering short texts using wikipedia . Proc. of the 30th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval . pp. 787 – 788 .
   Google Scholar
• Witten , I. H. , Paynter , G. W. , Frank , E. , Gutwin , C. and Nevill-Manning , C. G. KEA: Practical automatic keyphrase extraction . Proc. of the 4th ACM Conf. on Digital Libraries . pp. 255( 1999
   Google Scholar
• Turney , P. D. 1999 . “ Learning to extract keyphrases from text ” . In National Research Council, Institute for Information Technology, Technical Report ERB-1057
   Google Scholar
• Wu , X. and Bolivar , A. Keyword extraction for contextual advertisement . Proc. of the 17th Intl Conf. on World Wide Web . pp. 1195 – 1196 .
   Google Scholar
• Joachims , T. Optimizing search engines using clickthrough data . Proc. of the 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining . pp. 133 – 142 .
   Google Scholar
• Burges , C. , Shaked , T. , Renshaw , E. , Lazier , A. , Deeds , M. , Hamilton , N. and Hullender , G. Learning to rank using gradient descent . Proc. of the 22nd Intl. Conf. on Machine Learning . pp. 89 – 96 .
   Google Scholar
• Yu , S. , Yu , K. , Tresp , V. and Kriegel , H. P. Collaborative ordinal regression . Proc. of the 23rd Intl. Conf. on Machine learning . pp. 1089 – 1096 .
   Google Scholar
• Zhu , X. and Goldberg , A. Kernel regression with order preferences . Proc. of the 22nd AAAI Conf. on Artificial Intelligence . pp. 681 – 687 .
   Google Scholar
• Sun , J. , Zhao , W. , Xue , J. , Shen , Z. and Shen , Y. “Clustering with feature order preferences” . Proc. of the 10th Pacific Rim Intl. Conf. on Artificial Intelligence . pp. 382 – 393 .
   Google Scholar
• Ji , X. and Xu , W. Document clustering with prior knowledge . Proc. of the 29th Annual Intl. ACM SIGIR Conf. on Research and Development in Information Retrieval . pp. 405 – 412 .
   Google Scholar
• Chen , Y. , Rege , M. , Dong , M. and Hua , J. Incorporating user provided constraints into document clustering . Proc. of the 7th IEEE Intl. Conf. on Data Mining . pp. 103 – 112 .
   Google Scholar
• Hu , G. , Zhou , S. , Guan , J. and Hu , X. 2008 . Towards effective document clustering: A constrained k-means based approach . Inf. Process. Manage. , 44 ( 4 ) : 1397 – 1409 .
   Web of Science ®Google Scholar
• Boyd , S. and Vandenberghe , L. 2004 . Convex optimization , Cambridge University Press .
   Google Scholar
• Andersen , E. D. and Ye , Y. 1999 . On a homogeneous algorithm for the monotone complementarity problem . Mathematical Programming , 84 ( 2 ) : 375 – 399 .
   Web of Science ®Google Scholar
• Jain , A. K. and Dubes , R. C. 1988 . Algorithms for clustering data , Prentice-Hall, Inc .
   Google Scholar
• Halkidi , M. , Batistakis , Y. and Vazirgiannis , M. 2001 . On Clustering Validation Techniques . Journal of Intelligent Information Systems , 17 ( 2-3 ) : 107 – 145 .
   Web of Science ®Google Scholar
• Pfitzner , D. , Leibbrandt , R. and Powers , D. 2009 . Characterization and evaluation of similarity measures for pairs of clusterings . Knowl. Inf. Syst. , 19 : 361 – 394 .
   Web of Science ®Google Scholar
• Fern , X. Z. and Brodley , C. E. Random projection for high dimensional data clustering: A cluster ensemble approach . Prof. of the 20th Intl. Conf. on Machine Learning . pp. 186 – 193 .
   Google Scholar
• Fred , A. and Jain , A. Robust data clustering . Proc. of the 2003 IEEE Intl. Conf. on Computer Vision and Pattern Recognition . Vol. 2 , pp. 128 – 136 .
   Google Scholar
• Yin , X. , Hu , E. and Chen , S. 2008 . Discriminative semi-supervised clustering analysis with pairwise constraints . Journal of Software , 19 ( 11 ) : 2791 – 2802 . in Chinese
   Google Scholar
• Hu , X. , Zhang , X. , Lu , C. , Park , E. K. and Zhou , X. ExploitingWikipedia as external knowledge for document clustering . Proceedings of the 15th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining . pp. 389 – 396 .
   Google Scholar