Efficient Mining of High Confidence Association Rules without
Support Thresholds
Jinyan Li and Xiuzhen Zhang,
The University of Melbourne
Guozhu Dong,
Wright State University
Kotagiri Ramamohanarao and
Qun Sun,
The University of Melbourne
Proceedings of PKDD 99 -- 3rd European Conference on
Principles and Practice of Knowledge Discovery in Databases.
Prague, September 1999.
Abstract
Association rules describe the degree of dependence between items
in transactional datasets by their confidences.
In this paper, we first introduce the problem of mining
top rules, namely those association rules with 100% confidence.
Traditional approaches to this problem need a minimum
support (minsup) threshold and then can discover
the top rules with supports >= minsup;
such approaches, however, rely on minsup to
help avoid examining too many candidates and they
miss those top rules whose supports are below minsup.
The low support top rules
(e.g. some unusual combinations of some factors that have
always caused some disease) may be very interesting.
Fundamentally different from previous work, our proposed method
uses a dataset partitioning technique and two border-based
algorithms to efficiently discover all top rules with a given
consequent, without the constraint of support threshold.
Importantly, we use borders to concisely
represent all top rules, instead of enumerating them individually.
We also discuss how to discover all zero-confidence rules and
some very high (say 90%) confidence rules using
approaches similar to mining top rules.
Experimental results using the Mushroom, the Cleveland
heart disease, and the Boston housing datasets are
reported to evaluate the efficiency of the proposed approach.