Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimal structure identification with greedy search
The Journal of Machine Learning Research
A unified framework for model-based clustering
The Journal of Machine Learning Research
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
"Ideal Parent" structure learning for continuous variable networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Non-redundant Multi-view Clustering via Orthogonalization
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Simultaneous Unsupervised Learning of Disparate Clusterings
Statistical Analysis and Data Mining
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
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Search-based learning of latent tree models
Search-based learning of latent tree models
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
Discovering different types of topics: factored topic models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Localization of RFID-equipped assets during the operation phase of facilities
Advanced Engineering Informatics
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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Existing models for cluster analysis typically consist of a number of attributes that describe the objects to be partitioned and one single latent variable that represents the clusters to be identified. When one analyzes data using such a model, one is looking for one way to cluster data that is jointly defined by all the attributes. In other words, one performs unidimensional clustering. This is not always appropriate. For complex data with many attributes, it is more reasonable to consider multidimensional clustering, i.e., to partition data along multiple dimensions. In this paper, we present a method for performing multidimensional clustering on categorical data and show its superiority over unidimensional clustering.