Speed-up Iterative Frequent Itemset Mining with Constraint Changes

Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for the purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in enormous waste in time and in computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize the useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the contexts of two existing frequent itemset mining algorithms, FP-tree and Tree Projection. Experiment results on both synthetic and reallife datasets show that the proposed approach achieves dramatic saving in computation.