Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
On the Representation of Probabilities over Structured Domains
CAV '99 Proceedings of the 11th International Conference on Computer Aided Verification
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computers and Electronics in Agriculture
Supervised classification using probabilistic decision graphs
Computational Statistics & Data Analysis
Learning probabilistic decision graphs
International Journal of Approximate Reasoning
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs
International Journal of Approximate Reasoning
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Within data mining, clustering can be considered the most important unsupervised learning problem which deals with finding a structure in a collection of unlabeled data. Generally, clustering refers to the process of organizing objects into groups whose members are similar . Among clustering approaches, those methods based on probabilistic models have been extensively developed, such as Naïve Bayes (NB) with a latent class (cluster identifier) found via an EM algorithm. Probabilistic Decision Graphs (PDGs) are a class of graphical models that can naturally encode some context specific independencies that cannot always be efficiently captured by other commonly used models. In this paper we propose to use a mixture of PDG models in cluster discovery, and an algorithm for automatic induction of the mixture and the models is introduced. The proposed approach was experimentally evaluated on both synthetic and real-world databases, and the presentation of the results includes a comparison with related techniques. The comparison demonstrates competitive performance of the mixture of PDG models with respect to likelihood. Also, the mixture of PDG models have a tendency to use fewer models (clusters) to represent domains where other models use large amounts of clusters.