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Incremental knowledge discovering in interval-valued decision information system with the dynamic data

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Abstract

With the development of information technology and the rapid updating of data sets, the object sets in an information system may evolve in time when new information arrives and redundancy information leaves in real life. Interval-valued decision information systems are important type of data decision tables and generalized models of single-valued information systems. Fast updating the lower and upper approximations is the core technology of knowledge discovery that is based rough set theory in dynamic data environment. Consequently, in this paper we focus on incremental approaches updating approximations with dynamic data sets in interval-valued decision system. Firstly, we define an interval similarity degree by which a binary relation can be constructed, followed a rough set model be established. Then, incremental approaches for updating approximations are proposed and the incremental algorithms are shown. At last, comparative experiments on several UCI data sets show the proposed incremental updating methods are efficient and effective for dynamic data sets, namely, these approaches significantly outperform the classical methods with a dramatic reduction in the computational time.

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Acknowledgments

The author would like to thank the valuable suggestions from the anonymous referees and the editor in chief for improving the quality of the paper. This work is supported by Natural Science Foundation of China (No. 61105041, No. 61472463, No. 61402064), National Natural Science Foundation of CQ CSTC (No. cstc 2013jcyjA40051, cstc2015jcyjA40053), Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information (Nanjing University of Science and Technology), Ministry of Education (No. 30920140122006), Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province (No.OBDMA201503), and Graduate Innovation Foundation of Chongqing University of Technology (No.YCX2015227, YCX2014236), and Graduate Innovation Foundation of CQ (No.CYS15223).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Chongqing University of Technology, 400054, Chongqing, People’s Republic of China

    Jianhang Yu & Weihua Xu

  2. Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information, Ministry of Education, Nanjing University of Science and Technology, 210094, Nanjing, People’s Republic of China

    Weihua Xu

  3. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, 316022, Zhoushan, Zhejiang, People’s Republic of China

    Weihua Xu

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  1. Jianhang Yu
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  2. Weihua Xu
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Correspondence to Weihua Xu.

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Yu, J., Xu, W. Incremental knowledge discovering in interval-valued decision information system with the dynamic data. Int. J. Mach. Learn. & Cyber. 8, 849–864 (2017). https://doi.org/10.1007/s13042-015-0473-z

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  • DOI: https://doi.org/10.1007/s13042-015-0473-z

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