Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory

Increased application of structured pattern mining requires a perfect understanding of the problem and a clear identification of the advantages and disadvantages of existing algorithms. Among those algorithms, pattern-growth methods have been shown to have the best performance when applied to sequential pattern mining. However, their advantages over apriori-based methods are not well explained and understood. Detailed analysis of the performance and memory requirements for these algorithms shows that counting the support for each potential pattern is the most computationally demanding step. Additionally, the analysis makes clear that the main advantage of patterngrowth over apriori-based methods resides on the restriction of the search space that is obtained from the creation of projected databases. In this paper, we present this analysis and describe how apriori-based algorithms can achieve the efficiency of pattern-growth methods.