Factor probabilistic distance clustering (FPDC): a new clustering method
暂无分享,去创建一个
Marina Marino | Mireille Gettler-Summa | Francesco Palumbo | Cristina Tortora | F. Palumbo | C. Tortora | Marina Marino | M. Gettler-Summa
[1] S. Rachev,et al. The Methods of Distances in the Theory of Probability and Statistics , 2013 .
[2] Michio Yamamoto,et al. A GENERAL FORMULATION OF CLUSTER ANALYSIS WITH DIMENSION REDUCTION AND SUBSPACE SEPARATION , 2014 .
[3] Paul D. McNicholas,et al. Parsimonious Gaussian mixture models , 2008, Stat. Comput..
[4] Michelle A. Steane,et al. Model-Based Classification via Mixtures of Multivariate t-Factor Analyzers , 2012, Commun. Stat. Simul. Comput..
[5] J. Bezdek. Numerical taxonomy with fuzzy sets , 1974 .
[6] Ryan P. Browne,et al. A mixture of generalized hyperbolic factor analyzers , 2013, Advances in Data Analysis and Classification.
[7] Charles Bouveyron,et al. Model-based clustering of high-dimensional data: A review , 2014, Comput. Stat. Data Anal..
[8] H. Kiers,et al. Selecting among three-mode principal component models of different types and complexities: a numerical convex hull based method. , 2006, The British journal of mathematical and statistical psychology.
[9] E. Ceulemans,et al. Subspace K-means clustering , 2013, Behavior Research Methods.
[10] J. Carroll,et al. K-means clustering in a low-dimensional Euclidean space , 1994 .
[11] Robert LIN,et al. NOTE ON FUZZY SETS , 2014 .
[12] Geoffrey J. McLachlan,et al. Modelling high-dimensional data by mixtures of factor analyzers , 2003, Comput. Stat. Data Anal..
[13] Ryan P. Browne,et al. Parsimonious Shifted Asymmetric Laplace Mixtures , 2013, 1311.0317.
[14] Maurizio Vichi,et al. Clustering and disjoint principal component analysis , 2009, Comput. Stat. Data Anal..
[15] Cem Iyigun,et al. Probabilistic D-Clustering , 2008, J. Classif..
[16] Dimitris Karlis,et al. Model-based clustering with non-elliptically contoured distributions , 2009, Stat. Comput..
[17] P. Kroonenberg. Applied Multiway Data Analysis , 2008 .
[18] Rasmus Bro,et al. The N-way Toolbox for MATLAB , 2000 .
[19] P. McNicholas,et al. Extending mixtures of multivariate t-factor analyzers , 2011, Stat. Comput..
[20] C. Iyigun. Probabilistic Distance Clustering , 2011 .
[21] Pieter M. Kroonenberg,et al. Multiplicatieve decompositie van interacties bij oordelen over de werkelijkheidswaarde van televisiefilms [Multiplicative decomposition of interactions for judgements of realism of television films] , 1987 .
[22] P. Arabie,et al. Cluster analysis in marketing research , 1994 .
[23] Geoffrey J. McLachlan,et al. On mixtures of skew normal and skew $$t$$-distributions , 2012, Adv. Data Anal. Classif..
[24] Ryan P. Browne,et al. Mixtures of Variance-Gamma Distributions , 2013, 1309.2695.
[25] H. Kiers,et al. Three-mode principal components analysis: choosing the numbers of components and sensitivity to local optima. , 2000, The British journal of mathematical and statistical psychology.
[26] Paul D. McNicholas,et al. Parsimonious skew mixture models for model-based clustering and classification , 2013, Comput. Stat. Data Anal..
[27] Michel van de Velden,et al. Methods for joint dimension reduction and clustering , 2013 .
[28] Ruben H. Zamar,et al. Robust Estimates of Location and Dispersion for High-Dimensional Datasets , 2002, Technometrics.
[29] L. Tucker,et al. Some mathematical notes on three-mode factor analysis , 1966, Psychometrika.
[30] Alain Morineau,et al. Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age , 2008 .
[31] Henk A L Kiers,et al. A fast method for choosing the numbers of components in Tucker3 analysis. , 2003, The British journal of mathematical and statistical psychology.
[32] Hans-Hermann Bock,et al. On the Interface between Cluster Analysis, Principal Component Analysis, and Multidimensional Scaling , 1987 .
[33] H. Kiers,et al. Factorial k-means analysis for two-way data , 2001 .
[34] Heungsun Hwang,et al. An Extension of Multiple Correspondence Analysis for Identifying Heterogeneous Subgroups of Respondents , 2006 .
[35] Saskia de Craen,et al. Effects of Group Size and Lack of Sphericity on the Recovery of Clusters in K-means Cluster Analysis , 2006, Multivariate behavioral research.
[36] Paul D. McNicholas,et al. Capturing patterns via parsimonious t mixture models , 2013, 1303.2316.
[37] Ryan P. Browne,et al. Unsupervised learning via mixtures of skewed distributions with hypercube contours , 2014, Pattern Recognit. Lett..
[38] Mireille Gettler-Summa,et al. Factor PD-Clustering , 2013, Algorithms from and for Nature and Life.
[39] Ryan P. Browne,et al. Mixtures of skew-t factor analyzers , 2013, Comput. Stat. Data Anal..
[40] Adrian E. Raftery,et al. Linear flaw detection in woven textiles using model-based clustering , 1997, Pattern Recognit. Lett..
[41] Anil K. Jain. Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..
[42] Geoffrey J. McLachlan,et al. Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.
[43] Ajay K. Manrai,et al. A New Multidimensional Scaling Methodology for the Analysis of Asymmetric Proximity Data in Marketing Research , 1992 .
[44] Marina Marino,et al. Robustness and Stability Analysis of Factor PD-Clustering on Large Social Data Sets , 2014 .
[45] Geoffrey J. McLachlan,et al. Extending mixtures of factor models using the restricted multivariate skew-normal distribution , 2013, J. Multivar. Anal..
[46] Geoffrey E. Hinton,et al. The EM algorithm for mixtures of factor analyzers , 1996 .
[47] Michael Greenacre,et al. Exploratory data analysis leading towards the most interesting simple association rules , 2008, Comput. Stat. Data Anal..
[48] Maurizio Vichi,et al. A New Dimension Reduction Method: Factor Discriminant K-means , 2011, J. Classif..
[49] Anil K. Jain. Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..
[50] Paul D. McNicholas,et al. Variational Bayes approximations for clustering via mixtures of normal inverse Gaussian distributions , 2013, Advances in Data Analysis and Classification.
[51] Tsung I. Lin,et al. Maximum likelihood estimation for multivariate skew normal mixture models , 2009, J. Multivar. Anal..
[52] Tsung I. Lin,et al. Robust mixture modeling using multivariate skew t distributions , 2010, Stat. Comput..
[53] J. Vermunt. K-means may perform as well as mixture model clustering but may also be much worse: comment on Steinley and Brusco (2011). , 2011, Psychological methods.
[54] Alfred Ultsch,et al. Algorithms from and for Nature and Life - Classification and Data Analysis , 2013, Studies in Classification, Data Analysis, and Knowledge Organization.
[55] Charles Bouveyron,et al. Simultaneous model-based clustering and visualization in the Fisher discriminative subspace , 2011, Statistics and Computing.
[56] Eva Ceulemans,et al. Factorial and reduced K-means reconsidered , 2010, Comput. Stat. Data Anal..
[57] Boris G. Mirkin,et al. Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads , 2010, J. Classif..
[58] Geoffrey J. McLachlan,et al. Mixtures of Factor Analyzers , 2000, International Conference on Machine Learning.