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Content Provider | IEEE Xplore Digital Library |
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Author | Ozden, B. Ramaswamy, S. Silberschatz, A. |
Copyright Year | 1998 |
Description | Author affiliation: Inf. Sci. Res. Centre, Bell Labs., Murray Hill, NJ, USA (Ozden, B.) |
Abstract | We study the problem of discovering association rules that display regular cyclic variation over time. For example, if we compute association rules over monthly sales data, we may observe seasonal variation where certain rules are true at approximately the same month each year. Similarly, association rules can also display regular hourly, daily, weekly, etc., variation that is cyclical in nature. We demonstrate that existing methods cannot be naively extended to solve this problem of cyclic association rules. We then present two new algorithms for discovering such rules. The first one, which we call the sequential algorithm, treats association rules and cycles more or less independently. By studying the interaction between association rules and time, we devise a new technique called cycle pruning, which reduces the amount of time needed to find cyclic association rules. The second algorithm, which we call the interleaved algorithm, uses cycle pruning and other optimization techniques for discovering cyclic association rules. We demonstrate the effectiveness of the interleaved algorithm through a series of experiments. These experiments show that the interleaved algorithm can yield significant performance benefits when compared to the sequential algorithm. Performance improvements range from 5% to several hundred percent. |
Starting Page | 412 |
Ending Page | 421 |
File Size | 111210 |
Page Count | 10 |
File Format | |
ISBN | 0818682892 |
ISSN | 10636382 |
DOI | 10.1109/ICDE.1998.655804 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 1998-02-23 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Association rules Data analysis Data mining Displays Transaction databases Marketing and sales Database systems Process control |
Content Type | Text |
Resource Type | Article |
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