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Under consideration for publication in Knowledge and Information Systems WAR: Weighted Association Rules for Item Intensities
BibTeX
@MISC{Wang_underconsideration,
author = {Wei Wang and Jiong Yang and Philip Yu},
title = {Under consideration for publication in Knowledge and Information Systems WAR: Weighted Association Rules for Item Intensities},
year = {}
}
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Abstract
In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule, we call them weighted association rules (WAR). One example of such a rule might be 80% of the people buying more than 3 bottles of soda will also be likely to buy more than 4 packages of snack food, while a conventional association rule might just be 60% of people buying soda will be also likely to buy snack food. Thus WARs can not only improve the confidence of the rules, but also provide a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by first ignoring the weight and finding the frequent itemsets (via a traditional frequent itemset discovery algorithm), and followed by introducing the weight during the rule generation. Specifically, the rule generation is achieved by partitioning the weight domain space of each frequent itemset into fine grids, and then identifying the popular regions within the domain space to derive WARs. This approach does not only support the batch mode mining, i.e., finding WARs for the dataset, but also supports the interactive mode, i.e., finding and refining WARs for a given (set) of frequent itemset(s).
Keyphrases
weighted association rule item intensity information system war frequent itemset rule generation snack food association rule fine grid approach mine war traditional frequent itemset discovery algorithm effective target marketing refining war interactive mode frequent itemsets derive war interest intensity conventional association rule domain space weight parameter potential degree popular region traditional association rule problem batch mode mining weight domain space
