Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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A rule selection framework is proposed which classifies, selects, and filters out association rules based on the analysis of the rule structures. It was applied to real traffic accident data collected from local police stations. The rudimentary nature of the data required several passes of association rule mining to be performed, each with different sets of parameters, so that semantically interesting rules can be spotted from the pool of results. It was shown that the proposed framework could find candidate rules that offer some insight into the phenomena being studied.