Efficient dynamic mining of constrained frequent sets Laks V. S. Lakshmanan, Carson Kai-Sang Leung, and Raymond T. Ng   View Paper (PDF)   Return to December 2003, Volume 28, Number 4 Abstract Data mining is supposed to be an iterative and exploratory process. In this context, we are work- ing on a project with the overall objective of developing a practical computing environment for the human-centered exploratory mining of frequent sets. One critical component of such an envi- ronment is the support for the dynamic mining of constrained frequent sets of items. Constraints enable users to impose a certain focus on the mining process; dynamic means that, in the middle of the computation, users are able to (i) change (such as tighten or relax) the constraints and/or (ii) change the minimum support threshold, thus having a decisive influence on subsequent com- putations. In a real-life situation, the available buffer space may be limited, thus adding another complication to the problem. In this article, we develop an algorithm, called DCF, for Dynamic Constrained Frequent-set com- putation. This algorithm is enhanced with a few optimizations, exploiting a lightweight structure called a segment support map. It enables DCF to (i) obtain sharper bounds on the support of sets of items, and to (ii) better exploit properties of constraints. Furthermore, when handling dynamic changes to constraints, DCF relies on the concept of a delta member generating function, which gen- erates precisely the sets of items that satisfy the new but not the old constraints. Our experimental results show the effectiveness of these enhancements. ©2004 Association for Computing Machinery