Capturing Uncertainty in Associative Classification Model

Choo, Yun Huoy and Abu Bakar, Azuraliza and Muda, A. K. (2009) Capturing Uncertainty in Associative Classification Model. In: 2nd Conference on Data Mining and Optimization, 27-28 October 2009, Selangor, Malaysia.

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This paper aims to propose a weighted linguistic associative classification model for uncertainty data analysis using rough membership function. Transformation of quantitative association rules into linguistic representation can be achieved in discretizing the numerical interval into rough interval described with respective rough membership values. Transformation of linguistic information system is suggested prior to the frequent pattern discovery. Neither pruning of association rules nor classifier modelling is needed. The rough membership values of the each linguistic frequent item are composited to form the weighted associative classification rule. Simulated results on Iris Plant dataset were shown in the paper. The future work of the research will focus on implementing the model with more experimental dataset.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: associative classification, rough set theory, rough membership function, uncertainty
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
A General Works > AS Academies and learned societies (General)
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Yun-Huoy Choo
Date Deposited: 20 Oct 2011 07:24
Last Modified: 28 May 2015 02:16
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