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Title: Compressed hierarchical mining of frequent closed patterns from dense data sets
Authors: Ji, L.
Tan, K.-L. 
Tung, A.K.H. 
Keywords: Data mining
Dense data sets
Frequent closed patterns
Parallel mining
Issue Date: 2007
Citation: Ji, L., Tan, K.-L., Tung, A.K.H. (2007). Compressed hierarchical mining of frequent closed patterns from dense data sets. IEEE Transactions on Knowledge and Data Engineering 19 (9) : 1175-1187. ScholarBank@NUS Repository.
Abstract: This paper addresses the problem of finding frequent closed patterns (FCPs) from very dense data sets. 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: C-Miner 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 paralleled without incurring significant communication overhead. We have implemented C-Miner and B-Miner, and our performance study on synthetic data sets and real dense microarray data sets shows their effectiveness over existing schemes. We also report experimental results on parallel versions of these two methods. © 2007 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
ISSN: 10414347
DOI: 10.1109/TKDE.2007.1047
Appears in Collections:Staff Publications

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