Parametric Algorithms for Mining Share-Frequent Itemsets

Authors:
Brock Barber;Howard J. Hamilton
Affiliations:
-;-
Venue:
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Year:
2000

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Abstract

Itemset share, the fraction of some numerical total contributed by items when they occur in itemsets, has been proposed as a measure of the importance of itemsets in association rule mining. The IAB and CAC algorithms [4], are able to find share frequent itemsets that have infrequent subsets. These algorithms perform well but do not always find all possible share frequent itemsets. In this paper, we describe the incorporation of a threshold factor into these algorithms. The threshold factor can be used to increase the number of frequent itemsets found at a cost of an increase in the number of infrequent itemsets examined. The modified algorithms are tested on a large commercial database. Their behavior is examined using principles of classifier evaluation from machine learning.