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Abstract: We introduce the problem of mining generalized association rules. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. For example, given a taxonomy that says that jackets is-a outerwear is-a clothes, we may infer a rule that "people who buy outerwear tend to buy shoes". This rule may hold even if rules that "people who buy jackets tend to buy... (Update)
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BibTeX entry: (Update)
Ramakrishnan Srikant and Rakesh Agrawal. Mining Generalized Association Rules. In Proc. of the 21st Int'l Conference on Very Large Databases, Zurich, Switzerland, September 1995. http://citeseer.ist.psu.edu/srikant95mining.html More
@article{ srikant97mining,
author = "Ramakrishnan Srikant and Rakesh Agrawal",
title = "Mining generalized association rules",
journal = "Future Generation Computer Systems",
volume = "13",
number = "2--3",
pages = "161--180",
year = "1997",
url = "citeseer.ist.psu.edu/srikant95mining.html" }
Citations (may not include all citations):957 The Probabilistic Method (context) - Alon, Spencer - 1992 ACM DBLP
921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993 ACM DBLP
910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994 ACM
268 Mining generalized association rules - Srikant, Agrawal - 1995 ACM DBLP
125 An effective hash based algorithm for mining association rul.. - Park, Chen et al. - 1995 DBLP
121 Efficient algorithms for discovering association rules - Mannila, Toivonen et al. - 1994 DBLP
99 Information Processing Letters (context) - Hagerup, Rub et al. - 1989
47 Set-oriented mining of association rules (context) - Houtsma, Swami - 1995
13 and presentation of strong rules (context) - Piatetsky-Shapiro, analysis - 1991
The graph only includes citing articles where the year of publication is known.
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