Utility Guided Pattern Mining

Abstract

This work is an initial exploration of the use of the decision-theoretic concept of utility to guide pattern mining. We present the use of utility functions as against thresholds and constraints as the mechanism to express user preferences and formulate several pattern mining problems that use utility functions. Utility guided pattern mining provides the twin benefits of capturing user preferences precisely using utility functions and of expressing user focus by choosing an appropriate utility guided pattern mining problem. It addresses the drawbacks of threshold guided pattern mining, the specification of threshold and the assumption of a fixed level of interest. We examine the problem of mining patterns with the best utility values in detail. We examine monotonicity properties of utility functions and the composition of utility functions from sub-utility functions as mechanisms to prune the search space. We also present a top-down approach for generating projected databases from FP-Trees, which is an order of magnitude faster than methods proposed in the literature.