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Abstract: We consider the problem of mining association rules over interval data (that is, ordered data for which the separation between data points has meaning). We show that the measures of what rules are most important (also called rule interest) that are used for mining nominal and ordinal data do not capture the semantics of interval data. In the presence of interval data, support and confidence are no longer intuitive measures of the interest of a rule. We propose a new definition of interest for... (Update)
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BibTeX entry: (Update)
R.J. Miller and Y. Yang. Association rules over interval data. In Proc. 1997 ACMSIGMOD Int. Conf. Management of Data, pages 452--461, Tucson, Arizona, May 1997. http://citeseer.ist.psu.edu/miller97association.html More
@inproceedings{ miller97association,
author = "R. J. Miller and Y. Yang",
title = "Association rules over interval data",
pages = "452--461",
year = "1997",
url = "citeseer.ist.psu.edu/miller97association.html" }
Citations (may not include all citations):921 Mining Association Rules between Sets of Items in Large Data.. - Agrawal, Imielinksi et al. - 1993
208 Fast Algorithms for Mining Association Rules in Large Databa.. (context) - Agrawal, Srikant - 1994
46 A Database Interface for Clustering in Large Spatial Databas.. (context) - Ester, Kriegel et al.
30 Efficient Mining of Association Rules in Distributed Databas.. - Cheung, Ng et al. - 1996
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