(Enter summary)
Abstract: Association rules are a key data-mining tool and as such
have been well researched. So far, this research has
focused predominantly on databases containing categorical
data only. However, many real-world databases contain
quantitative attributes and current solutions for this case
are so far inadequate. We introduce a new definition of
quantitative association rules based on statistical inference
theory. Our definition reflects the intuition that the
goal of association rules is to find... (Update)
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BibTeX entry: (Update)
Aumann, Y.; Lindell, Y.: "A Statistical Theory for Quantitative Association Rules", Proceedings KDD99, San Diego, CA, 1999, pp. 261 - 270 http://citeseer.ist.psu.edu/aumann99statistical.html More
@inproceedings{ aumann99statistical,
author = "Yonatan Aumann and Yehuda Lindell",
title = "A Statistical Theory for Quantitative Association Rules",
booktitle = "Knowledge Discovery and Data Mining",
pages = "261-270",
year = "1999",
url = "citeseer.ist.psu.edu/aumann99statistical.html" }
Citations (may not include all citations):
921
Mining association rules between sets of items in large data..
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