FIExPat: flexible extraction of sequential patterns

Abstract

This paper addresses sequential data mining, a sub-area of data mining where the data to be analyzed is organized in sequences. In many problem domains a natural ordering exists over data. Examples of sequential databases (SDBs) include: (a) collections of temporal data sequences, such as chronological series of daily stock indices or multimedia data (sound, music, video, etc.); and (b) macromolecule banks, where amino acid or proteic sequences are represented as strings. In a SDB it is often valuable to detect regularities through one or several sequences. In particular, finding exact or approximate repetitions of segments can be utilized directly (e.g. for determining the biochemical activity of a protein region) or indirectly, e.g. for prediction in finance. To this end, we present concepts and an algorithm for automatically extracting sequential patterns from a sequential database. Such a pattern is defined as a group of significantly similar segments from one or several sequences. Appropriate functions for measuring similarity between sequence segments are proposed, generalizing the edit distance framework. There is a trade off between flexibility, particularly in sequence data representation and in associated similarity metrics, and computational efficiency. We designed the FlExPat algorithm to satisfactorily cope with this trade-off. FlExPat's complexity is in practice lesser than quadratic in the total length of the SDB analyzed, while allowing high flexibility. Some experimental results obtained with FlExPat on music data are presented and commented.