• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.709-717
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• 2010

The task of association rule mining is to find certain association relationships among a set of data items in a database. There are three primary measures for association rule, support and confidence and lift. In this paper we developed a association rule ranking function using conditional probability increment ratio. We compared our function with several association rule ranking functions by some numerical examples. As the result, we knew that our decision function was better than the existing functions. The reasons were that the proposed function of the reference value is not affected by a particular association threshold, and our function had a value between -1 and 1 regardless of the range for three association thresholds. And we knew that the ranking function using conditional probability increment ratio was very well reflected in the difference between association rule measures and the minimum association rule thresholds, respectively.