Mining Interesting and Contiguous Maximal Sequential Patterns on High Dimensional Sequences

Previous methods have presented convincing arguments that mining complete set of patterns is huge for effective usage. A compact but high quality set of patterns, such as closed patterns and maximal patterns is needed. Most of the previously maximal sequential pattern mining algorithms on high dimensional sequence, such as biological data set, work under the same support. In this paper, an efficient algorithm MM-Prefix Span (Maximal and Multi-Support-based Prefix Span) for mining maximal patterns based on multi-support is proposed. Thorough performances on Beta-globin gene sequences have demonstrated that MM-Prefix Span consumes less memory usage and runtime than Prefix Span. It generates compressed results and two kinds of interesting patterns.