Issue No. 03 - May/June (2002 vol. 14)
<p><b>Abstract</b>—Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. In this paper, we propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT—Sequential Pattern mIning with Regular expressIon consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like antimonotonicity) are integrated into the mining process. A quantitative exploration of these trade-offs is conducted through an extensive experimental study on synthetic and real-life data sets. The experimental results clearly validate the effectiveness of our approach, showing that speedups of more than an order of magnitude are possible when RE constraints are pushed deep inside the mining process. Our experimentation with real-life data also illustrates the versatility of REs as a user-level tool for focusing on interesting patterns.</p>
Data mining, constraints, sequential patterns, regular expressions, finite automata
M. Garofalakis, R. Rastogi and K. Shim, "Mining Sequential Patterns with Regular Expression Constraints," in IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. , pp. 530-552, 2002.