Title :
Minimum-entropy data partitioning using reversible jump Markov chain Monte Carlo
Author :
Roberts, Stephen J. ; Holmes, Chris ; Denison, Dave
Author_Institution :
Dept. of Eng. Sci., Oxford Univ., UK
fDate :
8/1/2001 12:00:00 AM
Abstract :
Problems in data analysis often require the unsupervised partitioning of a data set into classes. Several methods exist for such partitioning but many have the weakness of being formulated via strict parametric models (e.g., each class is modeled by a single Gaussian) or being computationally intensive in high-dimensional data spaces. We reconsider the notion of such cluster analysis in information-theoretic terms and show that an efficient partitioning may be given via a minimization of partition entropy. A reversible-jump sampling is introduced to explore the variable-dimension space of partition models
Keywords :
Markov processes; Monte Carlo methods; data analysis; minimum entropy methods; pattern clustering; cluster analysis; data analysis; information theory; minimum-entropy data partitioning; reversible jump Markov chain Monte Carlo; reversible-jump sampling; unsupervised data set partitioning; variable-dimension space; Bayesian methods; Computer Society; Data analysis; Entropy; Image sampling; Information analysis; Monte Carlo methods; Parametric statistics; Signal sampling; Space exploration;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on