Fast generation of sequential patterns with item constraints from concise representations

Abstract

Constraint-based frequent sequence mining is an important and necessary task in data mining since it shows results very close to the requirements and interests of users. Most existing algorithms for performing this task are based on a traditional approach that mines patterns directly from a sequence database (SDB). However, in fact, SDBs are often very large. The algorithms thus often exhibit poor performance because the number of generated candidates and the search space are enormous, especially for low minimum support thresholds. In addition, these algorithms must read an SDB again when a constraint is changed by the user. In the context of frequently varied constraints, repeatedly scanning SDBs consume much time. To address this issue, we propose a novel approach for generating frequent sequences with various constraints from the two sets of frequent closed sequences ($$ {{\mathcal{F}}{\mathcal{C}}{\mathcal{S}}} $$) and frequent generator sequences ($$ {{\mathcal{F}}{\mathcal{G}}{\mathcal{S}}} $$), which are the concise representations of the set $$ {{\mathcal{F}}{\mathcal{S}}} $$ of all frequent sequences. The proposed approach is based on novel theoretical results that show an explicit relationship between $$ {{\mathcal{F}}{\mathcal{S}}} $$ and these two sets and have been strictly proved. The approach is then used to develop an efficient algorithm named MFS-IC for quickly generating frequent sequences with item constraints, a task that has many real-life applications. Extensive experiments on real-life and synthetic databases show that the proposed MFS-IC algorithm outperforms state-of-the-art algorithms, which directly mine frequent sequences with constraints from an SDB, in terms of runtime, memory usage and scalability.