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Abstract
Because the data being mined in the temporal database will evolve with time, many researchers have focused on the incremental mining of frequent sequences in temporal database [3,8,9]. In this paper, we study the problem of discovering association rules that display regular cyclic variation over time. For example, bread and coffee are frequently sold together in morning hours, or moon cake, lantern and candle are often sold before Mid-autumn Festival. This paper extends the Apriori algorithm and develop the optimization technique for discovering cyclic association rules.
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Article Details
Issue: Vol 7 No 8 (2004)
Page No.: 12-19
Published: Aug 31, 2004
Section: Article
DOI: https://doi.org/10.32508/stdj.v7i8.3237
Copyright Info
Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Dinh Ngoc, N., Xuan Huy, N., & Dinh Thuan, N. (2004). USING CYCLIC ASSOCIATION RULES IN TEMPORAL DATABASE. Science and Technology Development Journal, 7(8), 12-19. https://doi.org/https://doi.org/10.32508/stdj.v7i8.3237
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