Constraint-based sequential pattern mining: the consideration of recency and compactness
Decision Support Systems
Twain: Two-end association miner with precise frequent exhibition periods
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Journal of Systems and Software
A Framework for Mining Fuzzy Association Rules from Composite Items
New Frontiers in Applied Data Mining
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Keeping the resident in the loop: adapting the smart home to the user
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining sequential patterns in the B2B environment
Journal of Information Science
Mining consequence events in temporal health data
Intelligent Data Analysis - Knowledge Discovery in Bioinformatics
An efficient algorithm for incremental mining of temporal association rules
Data & Knowledge Engineering
Learning automaton based on-line discovery and tracking of spatio-temporal event patterns
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Mining temporal indirect associations
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
An adaptive sensor mining framework for pervasive computing applications
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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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.