An Automata Approach to Pattern Collections.

Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a promising theoretical framework for data mining, and recently they have been studied actively. However, there has not been much research on how condensed representations should actually be represented. In this paper we study how condensed representations of frequent itemsets can be concretely represented: we propose the use of deterministic finite automata to represent pattern collections and study the properties of the automata representation. The automata representation supports visualization of the patterns in the collection and clustering of the patterns based on their structural properties and interestingness values. Furthermore, we show experimentally that finite automata provide a space-efficient way to represent itemset collections. [ABSTRACT FROM AUTHOR]