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Abstract: This paper further develops Aumann and Lindell's [3] proposal for a variant of association rules for which the consequent is a numeric variable. It is argued that these rules can discover useful interactions with numeric data that cannot be discovered directly using traditional association rules with discretization. Alternative measures for identifying interesting rules are proposed. Efficient algorithms are presented that enable these rules to be discovered for dense data sets for which... (Update)
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...statistics than counts. The price is increased expense compared with sparse positive literal learning. OPUS [ Webb, 1995, Webb, 2000, Webb, 2001 ] which we compare against, addresses a similar problem. For general database queries involving additive aggregates (sums of...
.... 14 Popular algorithms for mining association rules include AIS [12] Apriori [29] and OPUS AR [27] A new algorithm called OPUS IR [32] is also discussed. 2.4.3.1 AIS AIS [12] algorithm searches for rules that satisfy a minimum support and a minimum confidence. Rules only...
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0.8: Efficient Search for Association Rules - Webb (2000) (Correct)
0.5: Rule-space Search for Knowledge-based Discovery - Provost, al. (1999) (Correct)
0.4: On Detecting Differences Between Groups - Webb, Butler, Newlands (2003) (Correct)
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2: Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
2: Constraint-based rule mining on large (context) - Bayardo, Agrawal et al. - 1999
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BibTeX entry: (Update)
Geo rey I. Webb. Discovering associations with numeric variables. In Knowledge Discovery and Data Mining, 2001. http://citeseer.ist.psu.edu/rey01discovering.html More
@inproceedings{ webb01discovering,
author = "Geoffrey I. Webb",
title = "Discovering associations with numeric variables",
booktitle = "Knowledge Discovery and Data Mining",
pages = "383-388",
year = "2001",
url = "citeseer.ist.psu.edu/rey01discovering.html" }
Citations (may not include all citations):910 Fast algorithms for mining association rules - Agrawal, Srikant - 1994
696 UCI repository of machine learning databases (context) - Blake, Merz - 2001
209 Mining quantitative association rules in large relational ta.. - Srikant, Agrawal - 1996
137 Finding interesting rules from large sets of discovered asso.. - Klemettinen, Mannila et al. - 1999
121 Classi cation and Regression Trees (context) - Breiman, Friedman et al. - 1984
73 Mining the most interesting rules (context) - Bayardo, Agrawal - 1999
66 Search through systematic set enumeration (context) - Rymon - 1992
61 Mining associations between sets of items in massive databas.. (context) - Agrawal, Imielinski et al. - 1993
49 The UCI KDD archive (context) - Bay
32 Department of Information and Computer Science (context) - CA, California - 2001
24 Learning decision lists using homogeneous rules - Segal, Etzioni - 1994
19 Evaluating the interestingness of characteristic rules (context) - Kamber, Shinghal - 1995
18 A statistical theory for quantitative association rules - Aumann, Lindell - 1999
10 RL4: A tool for knowledge-based induction (context) - Clearwater, Provost - 1990
5 Brute-force mining of high-con dence classi cation rules - Bayardo - 1997
5 OPUS: An ecient admissible algorithm for unordered search (context) - Webb - 1995
4 Parallel branch-and-bound graph search for correlated associ.. - Morishita, Nakaya - 2000
4 Rule-space search for knowledge-based discovery - Provost, Aronis et al. - 1999
3 Ecient search for association rules (context) - Webb - 2000
3 Recent progress in learning decision lists by prepending inf.. - Webb - 1994
1 School of Computer Science Otto-von-Guericke-University of M.. (context) - Borgelt, Software - 2001
Documents on the same site (http://www.cm.deakin.edu.au/pubs/list_all_pubs_and_abs.asp?AID=1): More
Inclusive pruning: A new class of pruning rule for unordered.. - Webb (1996) (Correct)
Lazy Learning of Bayesian Rules - Zheng, Webb (Correct)
Efficient Search for Association Rules - Webb (2000) (Correct)
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