On the Length of Programs for Computing Finite Binary Sequences
Journal of the ACM (JACM)
Self-Organizing Maps
MML Markov classification of sequential data
Statistics and Computing
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
Single Factor Analysis in MML Mixture Modelling
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Discriminative, generative and imitative learning
Discriminative, generative and imitative learning
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
| Hi-index | 0.00 |
In Minimum Message Length (MML) clustering (unsupervised classification, mixture modelling) the aim is to infer a set of classes that best explains the observed data items. There are cases where parts of the observed data do not need to be explained by the inferred classes but can be used to improve the inference and resulting predictions. Our main contribution is to provide a simple and flexible way of using such context data in MML clustering. This is done by replacing the traditional mixing proportion vector with a new context matrix. We show how our method can be used to give evidence regarding the presence of apparent long-term trends in climate-related atmospheric pressure records. Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) solutions for our model have also been implemented to compare with the MML solution.