Efficiently Mining Closed Subsequences with Gap Constraints

Mining frequent subsequence patterns from sequence databases is a typical data mining problem and various e-cient sequential pattern mining algorithms have been proposed. In many problem domains (e.g, biology), the frequent subsequences conflned by the predeflned gap requirements are more meaningful than the general sequential patterns. In this paper we re-examine the closed sequential pattern mining problem by introduc- ing the gap constraints. The most challenging parts in this task include the constrained pattern closure check- ing and unpromising search space pruning. Inspired by some state-of-the-art closed or constrained sequential pattern mining algorithms, we propose an e-cient ap- proach to flnding the complete set of closed sequential patterns with gap constraints. The approach combines the newly devised constrained pattern closure checking scheme and pruning techniques with the pattern growth based subsequence enumeration framework. Our exten- sive performance study shows that our approach is very e-cient in mining frequent closed subsequences with gap constraints.