Abstract

Sequential Pattern Mining is an efficient technique for discovering recurring structures or patterns from very large datasetwidely addressed by the data mining community, with a very large field of applications, such as cross-marketing, DNA analysis, web log analysis,user behavior, sensor data, etc. The sequence pattern mining aims at extractinga set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan and PrefixSpan.In this paper, we are interested in the study of the impact of the pattern-growthordering on the performances of pattern growth-based sequential pattern mining algorithms.To this end, we introduce a class of pattern-growth orderings, called linear orderings, for which patterns are grown by making grow either the currentpattern prefix or the current pattern suffix from the same position at eachgrowth-step.We study the problem of pruning and partitioning the search space followinglinear orderings. Experimentations show that the order in which patternsgrow has a significant influence on the performances. 

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