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Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
July/August 2003 (vol. 15 no. 4)
pp. 1004-1017
ASCII Text
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Chang-Hung Lee, Ming-Syan Chen, Cheng-Ru Lin, "Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 4, pp. 1004-1017, July/August, 2003.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2003.1209015,
author = {Chang-Hung Lee and Ming-Syan Chen and Cheng-Ru Lin},
title = {Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {15},
number = {4},
issn = {1041-4347},
year = {2003},
pages = {1004-1017},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2003.1209015},
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 - Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules
IS - 4
SN - 1041-4347
SP1004
EP1017
EPD - 1004-1017
A1 - Chang-Hung Lee,
A1 - Ming-Syan Chen,
A1 - Cheng-Ru Lin,
PY - 2003
KW - Data mining
KW - general temporal association rule
KW - exhibition period
KW - publication database.
VL - 15
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 

Abstract—In this paper, we explore a new problem of mining general temporal association rules in publication databases. In essence, a publication database is a set of transactions where each transaction T is a set of items of which each item contains an individual exhibition period. The current model of association rule mining is not able to handle the publication database due to the following fundamental problems, i.e., 1) lack of consideration of the exhibition period of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm Progressive-Partition-Miner (abbreviated as PPM) to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2\hbox{-}{\rm{itemset}} based on the intrinsic partitioning characteristics. Algorithm PPM is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent 2\hbox{-}{\rm{itemsets}}. The feature that the number of candidate 2\hbox{-}{\rm{itemsets}} generated by PPM is very close to the number of frequent 2\hbox{-}{\rm{itemsets}} allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of PPM is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm PPM.

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Index Terms:
Data mining, general temporal association rule, exhibition period, publication database.
Citation:
Chang-Hung Lee, Ming-Syan Chen, Cheng-Ru Lin, "Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules," IEEE Transactions on Knowledge and Data Engineering, vol. 15, no. 4, pp. 1004-1017, July-Aug. 2003, doi:10.1109/TKDE.2003.1209015
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