Unsupervised Learning Using MML.
Jonathan Oliver, Rohan A. Baxter, Chris S. Wallace · 1996
This paper discusses the unsupervised learning problem. An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data. We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem, modifying an earlier such MML application. We give an empirical comparison of criteria prominent in the literature for estimating the number of components in a data set. We conclude that the Minimum Message Length criterion performs better than the alternatives on the data considered here for unsupervised learning tasks. 1 INTRODUCTION We discuss the unsupervised learning problem. There are many approaches to unsupervised learning. Within AI there have been systems such as (a) CLUSTER -- Michalski and Stepp [ 16 ] , (b) COBWEB -- Fisher [ 13 ] , (c) AQ17 -- Wnek and Michalski [ 29 ] , (d) AUTOCLASS -- Cheeseman et al. [ 7 ] and (e) Snob -- Wallace et al. [ 25; 26 ] which uses MML. Spat...