Mining closed flexible patterns in time-series databases

In this paper, we propose an efficient algorithm, called CFP, for mining closed flexible patterns in time-series databases, where flexible gaps are allowed in a pattern. Our proposed algorithm involves three stages: transforming a time-series database into a symbolic database, generating all frequent patterns of length one from the transformed database, and mining closed flexible patterns in a depth-first search manner. In the proposed method, we design two pruning strategies and a closure checking scheme to reduce the search space and thus speed up the algorithm. The experimental results show that our algorithm outperforms the modified Apriori algorithm by an order of magnitude.