On mining general temporal association rules in a publication database
Resource
Proceedings - IEEE International Conference on Data Mining, ICDM
Journal
IEEE International Conference on Data Mining, ICDM
Pages
337-344
Date Issued
2001-12
Date
2001-12
Author(s)
DOI
N/A
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, each containing an individual exhibition period. The current model of association rule mining is not able to handle a publication database due to the following fundamental problems: (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 (PPM), to discover general temporal association rules in a publication database. The basic idea of PPM is to first partition the publication database into exhibition periods of items and then progressively accumulate the occurrence count of each candidate 2-itemset based on the intrinsic partitioning characteristics. PPM is also designed to employ a filtering threshold in each partition to prune out those cumulatively infrequent 2-itemsets at an early stage. Explicitly, the execution time of PPM is, in orders of magnitude, smaller than those required by schemes which are directly extended from existing methods.
Type
conference paper
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