Mining frequent patterns from sequences is an important data mining problem which has direct applications in many areas. In this thesis, we make three contributions to the state-of-the-art of the sequential frequent pattern mining.

First of all, we propose a fast pattern-growth mining algorithm using a novel sequence database representation called First-Occurrence Linked WAP-tree (FLWAP-tree). The pattern-growth mining algorithm using the Pre-Order Linked WAP-tree (PLWAP-tree) was reported in the literature to be faster than other algorithms. We show that our pattern-growth using our FLWAP-tree outperforms the PLWAP-tree mining significantly and consistently.

Secondly, we extend the pattern-growth algorithm with partial enumeration so that the frequent patterns can grow with more than one symbol at a time. Our extended pattern-growth algorithm can be regarded as the one that blends both pattern-growth and apriori enumeration mining algorithms in one framework. We show that partial enumeration can speedup the pattern-growth mining when the depth of partial enumeration is properly controlled. Partial enumeration can also reduce the load imbalance among the parallel tasks when the pattern-growth mining algorithm is parallelized to run on parallel computers.

Lastly, we parallelize our pattern-growth mining algorithm using partial enumeration, and show that partial enumeration is essential to lift up the maximum speedup achievable on parallel computers.

In this thesis, we present the theory, algorithm design, implementation, and the experimental results for each of the contributions we make.