Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Temporal Features in Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Indexing and Mining of the Local Patterns in Sequence Database
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Distribution Discovery: Local Analysis of Temporal Rules
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering Local Patterns from Multiple Temporal Sequences
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
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In real world the knowledge used for aiding decision-making is always time varying. Most existing data mining approaches assume that discovered knowledge is valid indefinitely. Temporal features of the knowledge are not taken into account in mining models or processes. As a consequence, people who expect to use the discovered knowledge may not know when it became valid or whether it is still valid. This limits the usability of discovered knowledge. In this paper, temporal features are considered as important components of association rules for better decision-making. The concept of temporal association rules is formally defined and the problems of mining these rules are addressed. These include identification of valid time periods and identification of periodicities of an association rule, and mining of association rules with a specific temporal feature. A system has been designed and implemented for supporting the iterative process of mining temporal association rules, along with an interactive query and mining interface with an SQL-like mining language.