FLAME: Shedding light on hidden frequent patterns in sequence datasets

Existing database sequence mining algorithms focus on mining for subsequences. However, for many emerging applications, the subsequence model is inadequate for detecting interesting patterns. Often, an approximate substring model better accommodates the notion of a noisy pattern. In this paper, we present a powerful new model for approximate pattern mining. We show that this model can be used to capture the notion of an approximate match for a variety of different applications. We also present a novel, suffix tree based pattern mining algorithm called FLAME and demonstrate that it is a fast, accurate, and scalable method for discovering hidden patterns in large sequence databases. © 2008 IEEE.