Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Abstract

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is that the larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient.