Semi-supervised projected model-based clustering
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Concha Bielza | Pedro Larrañaga | Víctor Robles | Luis Guerra | C. Bielza | P. Larrañaga | Luis Guerra | V. Robles
[1] Adrian E. Raftery,et al. mclust Version 4 for R : Normal Mixture Modeling for Model-Based Clustering , Classification , and Density Estimation , 2012 .
[2] J. Friedman. Clustering objects on subsets of attributes , 2002 .
[3] M. Cugmas,et al. On comparing partitions , 2015 .
[4] Peter D. Hoff,et al. Model-based subspace clustering , 2006 .
[5] Arthur Zimek,et al. A survey on enhanced subspace clustering , 2013, Data Mining and Knowledge Discovery.
[6] Xiaojin Zhu,et al. Semi-Supervised Learning Literature Survey , 2005 .
[7] Peter D. Hoff,et al. Subset Clustering of Binary Sequences, with an Application to Genomic Abnormality Data , 2005, Biometrics.
[8] Nitesh V. Chawla,et al. Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains , 2011, J. Artif. Intell. Res..
[9] Jing Hua,et al. Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] D. Hand,et al. Clustering objects on subsets of attributes , 2004 .
[11] Philip S. Yu,et al. Finding generalized projected clusters in high dimensional spaces , 2000, SIGMOD '00.
[12] Michael K. Ng,et al. HARP: a practical projected clustering algorithm , 2004, IEEE Transactions on Knowledge and Data Engineering.
[13] Shili Lin,et al. Class discovery and classification of tumor samples using mixture modeling of gene expression data - a unified approach , 2004, Bioinform..
[14] Tomer Hertz,et al. Computing Gaussian Mixture Models with EM Using Equivalence Constraints , 2003, NIPS.
[15] 姜青山. Model-based Method for Projective Clustering , 2012 .
[16] Latifur Khan,et al. SISC: A Text Classification Approach Using Semi Supervised Subspace Clustering , 2009, 2009 IEEE International Conference on Data Mining Workshops.
[17] Huan Liu,et al. Subspace clustering for high dimensional data: a review , 2004, SKDD.
[18] George Kesidis,et al. Semisupervised mixture modeling with fine-grained component-conditional class labeling and transductive inference , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.
[19] Anil K. Jain,et al. Simultaneous feature selection and clustering using mixture models , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Hans-Peter Kriegel,et al. Density Based Subspace Clustering over Dynamic Data , 2011, SSDBM.
[21] Ranjan Maitra,et al. Simulating Data to Study Performance of Finite Mixture Modeling and Clustering Algorithms , 2010 .
[22] Volodymyr Melnykov,et al. Finite mixture models and model-based clustering , 2010 .
[23] Xianchao Zhang,et al. Constraint Based Dimension Correlation and Distance Divergence for Clustering High-Dimensional Data , 2010, 2010 IEEE International Conference on Data Mining.
[24] Myoung-Ho Kim,et al. FINDIT: a fast and intelligent subspace clustering algorithm using dimension voting , 2004, Inf. Softw. Technol..
[25] Martin Ester,et al. Robust projected clustering , 2007, Knowledge and Information Systems.
[26] T. Seidl,et al. ASCLU : Alternative Subspace Clustering , 2010 .
[27] Yi Zhang,et al. Entropy-based subspace clustering for mining numerical data , 1999, KDD '99.
[28] J. Friedman,et al. Clustering objects on subsets of attributes (with discussion) , 2004 .
[29] Xiaojin Zhu,et al. Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.
[30] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[31] David J. Miller,et al. Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection , 2006, IEEE Transactions on Signal Processing.
[32] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[33] Alexander Zien,et al. Semi-Supervised Learning , 2006 .
[34] Michael K. Ng,et al. On discovery of extremely low-dimensional clusters using semi-supervised projected clustering , 2005, 21st International Conference on Data Engineering (ICDE'05).
[35] Ian Witten,et al. Data Mining , 2000 .
[36] Christos Faloutsos,et al. Finding Clusters in subspaces of very large, multi-dimensional datasets , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).
[37] Jing Hua,et al. A Gaussian Mixture Model to Detect Clusters Embedded in Feature Subspace , 2007, Commun. Inf. Syst..
[38] T. M. Murali,et al. A Monte Carlo algorithm for fast projective clustering , 2002, SIGMOD '02.
[39] David J. Miller,et al. Joint Parsimonious Modeling and Model Order Selection for Multivariate Gaussian Mixtures , 2010, IEEE Journal of Selected Topics in Signal Processing.
[40] Kien A. Hua,et al. Constrained locally weighted clustering , 2008, Proc. VLDB Endow..
[41] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[42] Ian Davidson,et al. Constrained Clustering: Advances in Algorithms, Theory, and Applications , 2008 .
[43] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[44] Nizar Bouguila,et al. Model-based subspace clustering of non-Gaussian data , 2010, Neurocomputing.
[45] Helen C. Shen,et al. Semi-Supervised Classification Using Linear Neighborhood Propagation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[46] Philip S. Yu,et al. A Framework for Projected Clustering of High Dimensional Data Streams , 2004, VLDB.
[47] Arthur Zimek,et al. Clustering High-Dimensional Data , 2018, Data Clustering: Algorithms and Applications.
[48] Geoffrey J. McLachlan,et al. Mixture models : inference and applications to clustering , 1989 .
[49] Ashutosh Kumar Singh,et al. The EM Algorithm and Related Statistical Models , 2006, Technometrics.
[50] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[51] Arindam Banerjee,et al. Active Semi-Supervision for Pairwise Constrained Clustering , 2004, SDM.
[52] Aruna Tiwari,et al. Constructive Semi-Supervised Classification Algorithm and Its Implement in Data Mining , 2009, PReMI.
[53] Thomas Seidl,et al. Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data , 2012, KDD.
[54] Dimitrios Gunopulos,et al. Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.
[55] Xianchao Zhang,et al. Exploiting constraint inconsistence for dimension selection in subspace clustering: A semi-supervised approach , 2011, Neurocomputing.
[56] Joachim M. Buhmann,et al. Learning with constrained and unlabelled data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[57] Ira Assent,et al. HSM: Heterogeneous Subspace Mining in High Dimensional Data , 2009, SSDBM.
[58] Wei-Chen Chen,et al. MixSim: An R Package for Simulating Data to Study Performance of Clustering Algorithms , 2012 .
[59] Zhengdong Lu,et al. Semi-supervised Learning with Penalized Probabilistic Clustering , 2004, NIPS.
[60] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[61] Philip S. Yu,et al. Fast algorithms for projected clustering , 1999, SIGMOD '99.
[62] Céline Robardet,et al. Constraint-Based Subspace Clustering , 2009, SDM.
[63] Adrian E. Raftery,et al. How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..
[64] David J. Miller,et al. A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[65] Hans-Peter Kriegel,et al. Subspace clustering , 2012, WIREs Data Mining Knowl. Discov..