TED and EVA: Expressing temporal tendencies among quantitative variables using fuzzy sequential patterns

Temporal data can be handled in many ways for discovering specific knowledge. Sequential pattern mining is one of these relevant approaches when dealing with temporally annotated data. It allows discovering frequent sequences embedded in the records. In the access data of a commercial Web site, one may, for instance, discover that ldquo5% of the users request the page register.php 3 times and then request the page help.htmlrdquo. However, symbolic or fuzzy sequential patterns, in their current form, do not allow extracting temporal tendencies that are typical of sequential data. By means of temporal tendency mining, one may discover in the same access data that ldquoan increasing number of accesses to the register form preceeds an increasing number of accesses to the help page a few seconds laterrdquo. It would be easy to conclude that the users either quickly succeed in registering or make several attempts before they look at the help page within a few seconds. In this paper, we propose the definition of evolution patterns that allow discovering such knowledge. We show how to extract evolution patterns thanks to fuzzy sequential pattern mining techniques. We introduce our algorithms TED and EVA, designed for evolution pattern mining. Our proposal is validated by experiments and a sample of extracted knowledge is discussed.