Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Stable local computation with conditional Gaussian distributions
Statistics and Computing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient Learning of Hierarchical Latent Class Models
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Local Propagation in Conditional Gaussian Bayesian Networks
The Journal of Machine Learning Research
Penalized Model-Based Clustering with Application to Variable Selection
The Journal of Machine Learning Research
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A new feature selection method for Gaussian mixture clustering
Pattern Recognition
ACM Transactions on Knowledge Discovery from Data (TKDD)
Simultaneous Localized Feature Selection and Model Detection for Gaussian Mixtures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mixture-model cluster analysis using information theoretical criteria
Intelligent Data Analysis
Latent tree models and approximate inference in Bayesian networks
Journal of Artificial Intelligence Research
Latent tree models for multivariate density estimation: algorithms and applications
Latent tree models for multivariate density estimation: algorithms and applications
Greedy Learning of Binary Latent Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
Model-based multidimensional clustering of categorical data
Artificial Intelligence
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Variable selection is an important problem for cluster analysis of high-dimensional data. It is also a difficult one. The difficulty originates not only from the lack of class information but also the fact that high-dimensional data are often multifaceted and can be meaningfully clustered in multiple ways. In such a case the effort to find one subset of attributes that presumably gives the ''best'' clustering may be misguided. It makes more sense to identify various facets of a data set (each being based on a subset of attributes), cluster the data along each one, and present the results to the domain experts for appraisal and selection. In this paper, we propose a generalization of the Gaussian mixture models and demonstrate its ability to automatically identify natural facets of data and cluster data along each of those facets simultaneously. We present empirical results to show that facet determination usually leads to better clustering results than variable selection.