A framework for mining top-k frequent closed itemsets using order preserving generators
Proceedings of the 2nd Bangalore Annual Compute Conference
An Efficient Algorithm for Maintaining Frequent Closed Itemsets over Data Stream
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
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This paper addresses the problem of finding frequent closed patterns (FCPs) from very dense datasets. We introduce two compressed hierarchical FCP mining algorithms C-Miner and B-Miner. The two algorithms compress the original mining space, hierarchically partition the whole mining task into independent subtasks and mine each subtask progressively. The two algorithms adopt different task-partitioning strategies: CMiner partitions the mining task based on Compact Matrix Division whereas B-Miner partitions the task based on Base Rows Projection. The compressed hierarchical mining algorithms enhance the mining efficiency and facilitate a progressive refinement of results. Moreover, because the subtasks can be mined independently, C-Miner and B-Miner can be readily parallelized without incurring significant communication overhead. We have implemented C-Miner and B-Miner, and our performance study on synthetic datasets and real dense microarray datasets shows their effectiveness over existing schemes. We also report experimental results on parallel versions of these two methods.