Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Computing Association Rules Using Partial Totals
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Updated Bibliography of Temporal, Spatial, and Spatio-temporal Data Mining Research
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
Data Structure for Association Rule Mining: T-Trees and P-Trees
IEEE Transactions on Knowledge and Data Engineering
An effective mining approach for up-to-date patterns
Expert Systems with Applications: An International Journal
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
Temporal data mining with up-to-date pattern trees
Expert Systems with Applications: An International Journal
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The real transactional databases often exhibit temporal characteristic and time varying behavior. Temporal association rule has thus become an active area of research. A calendar unit such as months and days, clock units such as hours and seconds and specialized units such as business days and academic years, play a major role in a wide range of information system applications. The calendar-based pattern has already been proposed by researchers to restrict the time-based associationships. This paper proposes a novel algorithm to find association rule on time dependent data using efficient T tree and P-tree data structures. The algorithm elaborates the significant advantage in terms of time and memory while incorporating time dimension. Our approach of scanning based on time-intervals yields smaller dataset for a given valid interval thus reducing the processing time. This approach is implemented on a synthetic dataset and result shows that temporal TFP tree gives better performance over a TFP tree approach.