Mining Quantitative Association Rules in Large Relational Tables.
Ramakrishnan Srikant, Rakesh Agrawal:
Mining Quantitative Association Rules in Large Relational Tables.
SIGMOD Conference 1996: 1-12@inproceedings{DBLP:conf/sigmod/SrikantA96,
author = {Ramakrishnan Srikant and
Rakesh Agrawal},
editor = {H. V. Jagadish and
Inderpal Singh Mumick},
title = {Mining Quantitative Association Rules in Large Relational Tables},
booktitle = {Proceedings of the 1996 ACM SIGMOD International Conference on
Management of Data, Montreal, Quebec, Canada, June 4-6, 1996},
publisher = {ACM Press},
year = {1996},
pages = {1-12},
ee = {http://doi.acm.org/10.1145/233269.233311, db/conf/sigmod/SrikantA96.html},
crossref = {DBLP:conf/sigmod/96},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
Abstract
We introduce the problem of mining association rules in large relational tables
containing both quantitative and categorial attributes.
An example of such an association might be "10% of married people between age
50 and 60 have at least 2 cars".
We deal with quantitative attributes by fine-partitioning the values of the
attribute and then combining adjacent partitions as necessary.
We introduce measures of partial completeness which quantify the information lost due
to partitioning.
A direct application of this technique can generate too many similar rules.
We tackle this problem by using a "greater-than-expected-value"
interest measure to identify the interesting rules in the output.
We give an algorithm for mining such quantitative association rules.
Finally, we describe the results of using this approach on a real-life dataset.
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Printed Edition
H. V. Jagadish, Inderpal Singh Mumick (Eds.):
Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996.
ACM Press 1996 
,
SIGMOD Record 25(2),
June 1996
Contents
[Index Terms]
[Full Text in PDF Format, 1189 KB]
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A Data Mining Application: Customes Retention at the Port of Singapore Authority (PSA).
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SIGMOD Conference 1998: 13-24
- Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, Prabhakar Raghavan:
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications.
SIGMOD Conference 1998: 94-105
- Charu C. Aggarwal, Philip S. Yu:
A New Framework For Itemset Generation.
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Efficient Construction of Regression Trees with Range and Region Splitting.
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A One-Pass Algorithm for Accurately Estimating Quantiles for Disk-Resident Data.
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Association Rules over Interval Data.
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Methods and Problems in Data Mining.
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Clustering Association Rules.
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Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization.
SIGMOD Conference 1996: 13-23
- Takeshi Fukuda, Yasuhiko Morimoto, Shinichi Morishita, Takeshi Tokuyama:
Mining Optimized Association Rules for Numeric Attributes.
PODS 1996: 182-191
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