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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... (Update)
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BibTeX entry: (Update)
B. Ozden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. In Proc. 1998 Int. Conf. Data Engineering (ICDE'98), pages 412--421, Orlando, FL, Feb. 1998. http://citeseer.ist.psu.edu/ozden98cyclic.html More
@inproceedings{ ozden98cyclic,
author = "Banu Ozden and Sridhar Ramaswamy and Abraham Silberschatz",
title = "Cyclic Association Rules",
booktitle = "{ICDE}",
pages = "412-421",
year = "1998",
url = "citeseer.ist.psu.edu/ozden98cyclic.html" }
Citations (may not include all citations):921 Mining Association Rules between Sets of Items in Large Data.. - Agrawal, Imielinski et al. - 1993
268 Mining Generalized Association Rules - Srikant, Agrawal - 1995
208 Fast Algorithms for Mining Association Rules in Large Databa.. (context) - Agrawal, Srikant - 1994
189 Sampling Large Databases for Association Rules - Toivonen - 1996
164 An Efficient Algorithm for Mining Association Rules in Large.. (context) - Savasere, Omiecinski et al. - 1995
125 An Effective Hashbased Algorithm for Mining Association Rule.. - Park, Chen et al. - 1995
74 Data Mining Using TwoDimensional Optimized Association Rules (context) - Fukuda, Morimoto et al. - 1996
6 Discovery of Multi-level Association Rules From Large Databa.. (context) - Han, Fu - 1995
2 Mining Quantitative Association Rules (context) - Srikant, Agrawal - 1996
1 Calendric association rules (context) - Ramaswamy, Mahajan et al. - 1997
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