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A framework for interestingness measures for association rules with discrete and continuous attributes based on statistical validity


Mohd Shaharanee, Izwan Nizal and Mohd Jamil, Jastini (2015) A framework for interestingness measures for association rules with discrete and continuous attributes based on statistical validity. In: Artificial Intelligence in Theory and Practice IV. Springer International Publishing, pp. 119-128. ISBN 978-3-319-25260-5

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Abstract

Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. Furthermore, considering that many interestingness measures exist, we propose and compare two established approaches in selecting relevant attributes for the rules prior to rule generation. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non-significant rules. In addition to that, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with mixture of measurement level throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.

Item Type: Book Section
Additional Information: Book Subtitle: 4th IFIP TC 12 International Conference on Artificial Intelligence, IFIP AI 2015, Held as Part of WCC 2015, Daejeon, South Korea, October 4-7, 2015, Proceedings
Uncontrolled Keywords: Data mining; Interesting rules; Statistical analysis
Subjects: Q Science > QA Mathematics > QA76 Computer software
Divisions: School of Quantitative Sciences
Depositing User: Mr. Izwan Nizal Mohd Shaharanee
Date Deposited: 17 Apr 2016 02:33
Last Modified: 17 Apr 2016 02:33
URI: http://repo.uum.edu.my/id/eprint/17877

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