A taxonomy of Sequential Pattern Mining Algorithms

Due to the important applications in today's world such as mining web page traversal sequences, users behavior in buying or disease treatments, many algorithms have beenintroduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support brief representations like closed, maximal, incremental or hierarchical sequences. This article reviews a number of algorithms in each category and putsthem in taxonomy of sequential pattern mining techniques as an application. This article investigates these algorithms by introducing taxonomy for classifying sequential pattern mining algorithms based on their theoretical features and say advantage/disadvantage of them.This classification aims at enhancing understanding of sequential pattern mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques in time execution and memory usage.

Keywords:

Data mining , frequent sequential patterns , apriori-base , pattern-Growth , projected database