Mining Weighted Association Rules without Preassigned Weights

Authors:
Ke Sun;Fengshan Bai
Affiliations:
-;-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
2008

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Abstract

Association rule mining is a key issue in data mining. However the classical models ignore the difference between the transactions; and the weighted association rule mining does not work on databases with only binary attributes. In this paper, we introduce a new measure wsupport, which does not require pre-assigned weights. It takes the quality of transactions into consideration, using link-based models. A fast miming algorithm is given and a large amount of experimental results is presented.