Smart support functions for sequential pattern mining

Abstract

In real applications, transaction data typically contain quantitative attributes. Existing approaches and algorithms proposed for sequential pattern mining such as AprioriAll often assume Boolean attributes (i.e., quantitative values are simply interpreted/transformed as Boolean values). This article addresses the impact of varied quantitative attributes in sequential pattern mining. More specifically, we define alternate smart support functions for computing the support measure of candidate sequential patterns. A noticeable advantage of this work is that the proposed smart support functions can be smoothly integrated into the framework of existing sequential pattern mining algorithms. In the discussion of this article, we assume adoption of the well-known AprioriAll algorithm and discuss the incorporation of the proposed smart support functions into this framework. The expected mining results are believed better reflecting the particular interests of different user groups and thus are more satisfactory to the intended users.