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Issue No.04 - July/August (2003 vol.15)
pp: 1004-1017
<p><b>Abstract</b>—In this paper, we explore a new problem of mining <it>general temporal association rules</it> in <it>publication databases</it>. In essence, a publication database is a set of transactions where each transaction <tmath>T</tmath> 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 <it>exhibition period</it> of each individual item and 2) lack of an equitable support counting basis for each item. To remedy this, we propose an innovative algorithm <it>Progressive-Partition-Miner</it> (abbreviated as <it>PPM</it>) to discover general temporal association rules in a publication database. The basic idea of <it>PPM</it> is to first partition the publication database in light of exhibition periods of items and then progressively accumulate the occurrence count of each candidate <tmath>2\hbox{-}{\rm{itemset}}</tmath> based on the intrinsic partitioning characteristics. Algorithm <it>PPM</it> is also designed to employ a filtering threshold in each partition to early prune out those cumulatively infrequent <tmath>2\hbox{-}{\rm{itemsets}}</tmath>. The feature that the number of candidate <tmath>2\hbox{-}{\rm{itemsets}}</tmath> generated by <it>PPM</it> is very close to the number of frequent <tmath>2\hbox{-}{\rm{itemsets}}</tmath> allows us to employ the scan reduction technique to effectively reduce the number of database scans. Explicitly, the execution time of <it>PPM</it> is, in orders of magnitude, smaller than those required by other competitive schemes that are directly extended from existing methods. The correctness of <it>PPM</it> is proven and some of its theoretical properties are derived. Sensitivity analysis of various parameters is conducted to provide many insights into Algorithm <it>PPM</it>.</p>
Data mining, general temporal association rule, exhibition period, publication database.
Ming-Syan Chen, Chang-Hung Lee, "Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules", IEEE Transactions on Knowledge & Data Engineering, vol.15, no. 4, pp. 1004-1017, July/August 2003, doi:10.1109/TKDE.2003.1209015
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