Interestingness is Not a Dichotomy: Introducing Softness in Constrained Pattern Mining

Abstract

AbstractThe paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focussed on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. As a solution, in this paper we introduce the new paradigm of pattern discovery based on Soft Constraints. Albeit simple, the proposed paradigm overcomes all the major methodological drawbacks of the classical constraint-based paradigm, representing an important step further towards practical pattern discovery.KeywordsPattern MiningFrequent ItemsetsMining Association RuleSoft ConstraintPattern DiscoveryThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.