13th IEEE International Conference on Data Mining pages:557-566
IEEE International Conference on Data Mining edition:13 location:Dallas, Texas, USA date:7-10 December 2013
Finding small sets of interesting patterns is an
important challenge in pattern mining. In this paper, we argue
that several well-known approaches that address this challenge
are based on performing pairwise comparisons between patterns.
Examples include finding closed patterns, free patterns, relevant
subgroups and skyline patterns. Although progress has been
made on each of these individual problems, a generic approach
for solving these problems (and more) is still lacking. This paper
tackles this challenge. It proposes a novel, generic approach for
handling pattern mining problems that involve pairwise compar-
isons between patterns. Our key contributions are the following.
First, we propose a novel algebra for programming pattern
mining problems. This algebra extends relational algebras in
a novel way towards pattern mining. It allows for the generic
combination of constraints on individual patterns with dominance
relations between patterns. Second, we introduce a modified
generic constraint satisfaction system to evaluate these algebraic
expressions. Experiments show that this generic approach can
indeed effectively identify patterns expressed in the algebra.