Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets

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 indep...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2007-09, Vol.19 (9), p.1175-1187
Main Authors: Liping Ji, Kian-Lee Tan, Tung, A.K.H.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Association rules
Biological and medical sciences
Computer science
control theory
systems
Computer Society
Data analysis
Data mining
Data processing. List processing. Character string processing
dense datasets
Exact sciences and technology
Frequent closed patterns
Fundamental and applied biological sciences. Psychology
Gene expression
Memory organisation. Data processing
Mining
Molecular and cellular biology
Molecular genetics
parallel mining
Partitioning algorithms
Pattern analysis
progressive
Software
Studies
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Summary: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.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2007.1047