BAMBOO: Accelerating Closed Itemset Mining by Deeply Pushing the Length-Decreasing Support Constraint

Previous study has shown that mining frequent patterns with length-decreasing support constraint is very helpful in removing some uninteresting patterns based on the observation that short patterns will tend to be interesting if they have a high support, whereas long patterns can still be very interesting even if their support is relatively low. However, a large number of non-closed (i.e., redundant) patterns can still not be filtered out by simply applying the lengthdecreasing support constraint. As a result, a more desirable pattern discovery task could be mining closed patterns under the length-decreasing support constraint. In this paper we study how to push deeply the lengthdecreasing support constraint into closed itemset mining, which is a particularly challenging problem due to the fact that the downward-closure property cannot be used to prune the search space. Therefore, we have proposed several pruning methods and optimization techniques to enhance the closed itemset mining...

• Type: Conference
• Year: 2004
• Where: SDM
• Authors: Jianyong Wang, George Karypis
• Keywords: Closed Itemset Mining, Data Mining, Length-decreasing Support Constraints, SDM 2004, Support Constraints