On Mining General Temporal Association Rules in a Publication Database
Date Issued
2001-11
Date
2001-11
Author(s)
Lee, Chang-Hung
Lin, Cheng-Ru
Chen, Ming-Syan
DOI
20060927122754523633
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; (2) lack of an equitable support
counting basis for each item. To remedy this, we propose
an innovative algorithm Progressive-Partition-Miner
(abbreviatedly 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-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-itemsets. Explicitly, the execution time of PPM is,
in orders of magnitude, smaller than those required by the
schemes which are directly extended from existing methods.
Publisher
臺北市:國立臺灣大學電機工程學系
Type
thesis
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