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Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets
September 2007 (vol. 19 no. 9)
pp. 1175-1187
ASCII Text
x 
 
Liping Ji, Kian-Lee Tan, Anthony Tung, "Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 9, pp. 1175-1187, September, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2007.1047,
author = {Liping Ji and Kian-Lee Tan and Anthony Tung},
title = {Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {19},
number = {9},
issn = {1041-4347},
year = {2007},
pages = {1175-1187},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2007.1047},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets
IS - 9
SN - 1041-4347
SP1175
EP1187
EPD - 1175-1187
A1 - Liping Ji,
A1 - Kian-Lee Tan,
A1 - Anthony Tung,
PY - 2007
KW - Frequent closed patterns
KW - progressive
KW - dense datasets
KW - data mining
KW - parallel mining
VL - 19
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
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.

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Index Terms:
Frequent closed patterns, progressive, dense datasets, data mining, parallel mining
Citation:
Liping Ji, Kian-Lee Tan, Anthony Tung, "Compressed Hierarchical Mining of Frequent Closed Patterns from Dense Data Sets," IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 9, pp. 1175-1187, Sept. 2007, doi:10.1109/TKDE.2007.1047
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