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Rule Generation Based on Novel Two-Stage Model

Author

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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)

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.

Suggested Citation

  • Kuo-Ping Lin & Ching-Lin Lin & Yu-Ming Lu & Ping-Feng Pai, 2013. "Rule Generation Based on Novel Two-Stage Model," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
  • Handle: RePEc:tkp:tiim13:s5_39-60
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    References listed on IDEAS

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    1. Inuiguchi, Masahiro & Miyajima, Takuya, 2007. "Rough set based rule induction from two decision tables," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1540-1553, September.
    2. Yu, Hui-Kuang, 2005. "Weighted fuzzy time series models for TAIEX forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 609-624.
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    Cited by:

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    2. Dean, Mark & Kıbrıs, Özgür & Masatlioglu, Yusufcan, 2017. "Limited attention and status quo bias," Journal of Economic Theory, Elsevier, vol. 169(C), pages 93-127.
    3. Yoram Halevy & Dotan Persitz & Lanny Zrill, 2018. "Parametric Recoverability of Preferences," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1558-1593.
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    5. Nishimura, Hiroki & Ok, Efe A., 2016. "Utility representation of an incomplete and nontransitive preference relation," Journal of Economic Theory, Elsevier, vol. 166(C), pages 164-185.
    6. Zhu, Hui & Huang, Cheng & Lu, Rongxing & Li, Hui, 2016. "Modelling information dissemination under privacy concerns in social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 53-63.

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