Abstract
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.
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Verma, K., Vyas, O.P., Vyas, R. (2005). Temporal Approach to Association Rule Mining Using T-Tree and P-Tree. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_64
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DOI: https://doi.org/10.1007/11510888_64
Publisher Name: Springer, Berlin, Heidelberg
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