Compression picks item sets that matter

Abstract

Finding a comprehensive set of patterns that truly captures the characteristics of a database is a complicated matter. Frequent item set mining attempts this, but low support levels often result in exorbitant amounts of item sets. Recently we showed that by using MDL we are able to select a small number of item sets that compress the data well [11]. Here we show that this small set is a good approximation of the underlying data distribution. Using the small set in a MDL-based classifier leads to performance on par with well-known rule-induction and association-rule based methods. Advantages are that no parameters need to be set manually and only very few item sets are used. The classification scores indicate that selecting item sets through compression is an elegant way of mining interesting patterns that can subsequently find use in many applications. © Springer-Verlag Berlin Heidelberg 2006.