Rule Generation Based on Novel Two-Stage Model

Kuo-Ping Lin, Ching-Lin Lin, Yu-Ming Lu and Ping-Feng Pai
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Kuo-Ping Lin: Lunghwa University of Science and Technology, Taiwan
Ching-Lin Lin: Lunghwa University of Science and Technology, Taiwan
Yu-Ming Lu: Lunghwa University of Science and Technology, Taiwan
Ping-Feng Pai: National Chi Nan University, Taiwan

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Abstract: Purpose – The purpose of this paper is to develop a novel two-stage model for promoting the effect of rule generation based on rough set. In order to improve traditional rough set method, the novel two-stage model adopts new kernel intuitionistic fuzzy clustering (KIFCM) to promote performance of rough set theory. Moreover, the e-learning customer data set in Taiwan is also examined for demonstrate the effectiveness and practicality of model. Design/methodology/approach – In this paper, the authors present a new kernel intuitionistic fuzzy rough set model which combines novel KIFCM with rough set. The rule generation can divide to two stages for effective rule generation. In the first stage, KIFCM can utilize the advantages of kernel function and intuitionistic fuzzy sets to cluster raw data into similarity groups. In the second stage, the rough set theory is employed to generate rules with different groups. Finally, based on decision rules of rough set with different groups the results of system can be obtained and analyzed for users. Findings – The novel rule generation model adopts pre-process, which is KIFCM clustering technique, can effectively assist traditional rough set in promoting the performance. In analysis of e-learning data set, the empirical result indicates that proposed novel rule generation model can outperform traditional decision models. Practical implications –This novel two-stage model can provide a new and effective technique for data mining, database system, …, etc. Furthermore, in the research, proposed model also practically was applied to analyze and model customer’s tendency in e-learning platform with proper decision rules. Originality/value – The rough set theory has widely used in dealing with data mining and classification problems. This research proposes a construct of novel two-stage model which can effectively improve traditional rough set theory by using KIFCM clustering technology in the first stage. Real e-learning data set also is employed for demonstrate the effectiveness and practical. Based on the empirical result, the novel two-stage model can be evidenced that can actually apply in real information platform.

Keywords: rule generation; rough set; kernel intuitionistic fuzzy clustering; e-learning (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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