Bayesian Learning of Clique Tree Structure
This addresses the fundamental difficulties of probability modeling in high-dimensional categorical data, offering a method for deriving probability distributions, but it appears incremental as it builds on existing clique tree concepts.
The paper tackles the problem of categorical data analysis in high dimensions by presenting a solution based on Bayesian learning of clique tree decomposition, resulting in an automated determination of optimal clique tree structure and a unified approach to clustering and anomaly detection.
The problem of categorical data analysis in high dimensions is considered. A discussion of the fundamental difficulties of probability modeling is provided, and a solution to the derivation of high dimensional probability distributions based on Bayesian learning of clique tree decomposition is presented. The main contributions of this paper are an automated determination of the optimal clique tree structure for probability modeling, the resulting derived probability distribution, and a corresponding unified approach to clustering and anomaly detection based on the probability distribution.