An efficient algorithm for mining frequent sequences by a new strategy without support counting

Abstract

Mining sequential patterns in large databases is an important research topic. The main challenge of mining sequential patterns is the high processing cost due to the large amount of data. We propose a new strategy called direct sequence comparison (abbreviated as DISC), which can find frequent sequences without having to compute the support counts of nonfrequent sequences. The main difference between the DISC strategy and the previous works is the way to prune nonfrequent sequences. The previous works are based on the antimonotone property, which prune the nonfrequent sequences according to the frequent sequences with shorter lengths. On the contrary, the DISC strategy prunes the nonfrequent sequences according to the other sequences with the same length. Moreover, we summarize three strategies used in the previous works and design an efficient algorithm called DISC-all to take advantages of all the four strategies. The experimental results show that the DISC-all algorithm outperforms the PrefixSpan algorithm on mining frequent sequences in large databases. In addition, we analyze these strategies to design the dynamic version of our algorithm, which achieves a much better performance.