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A regression-based approach for improving the association rule mining through predicting the number of rules on general datasets
Faculty of Informatics - Papers (Archive)
  • Dien Tuan Le, University of Wollongong
  • Fenghui Ren, University of Wollongong
  • Minjie Zhang, University of Wollongong
RIS ID
69521
Publication Date
1-1-2012
Publication Details

Le, D., Ren, F. & Zhang, M. (2012). A regression-based approach for improving the association rule mining through predicting the number of rules on general datasets. Lecture Notes in Computer Science, 7458 (2012), 229-240.

Abstract

Association rule mining is one of the useful techniques in data mining and knowledge discovery that extracts interesting relationships between items in datasets. Generally, the number of association rules in a particular dataset mainly depends on the measures of support and confidence To choose the number of useful rules, normally, the measures of support and confidence need to be tried many times. In some cases, the measures of support and confidence are chosen by experience. Thus, it is a time consuming to find the optimal measure of supportand confidence for the process of association rule mining in large datasets. This paper proposes a regression based approach to improve the association rule mining process through predicting the number of rules on datasets. The approach includes a regression model in a generic level for general domains and an instantiation scheme to create concrete models in particular domains for predicting the potential number of association rules on a dataset before mining. The proposed approach can be used in broad domains with different types of datasets to improve the association rule mining process. A case study to build a concrete regression model based on a real dataset is demostrated and the result shows the good performance of the proposed approach.

Citation Information
Dien Tuan Le, Fenghui Ren and Minjie Zhang. "A regression-based approach for improving the association rule mining through predicting the number of rules on general datasets" (2012) p. 229 - 240
Available at: http://works.bepress.com/fren/5/