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Y. G. Zhang and C. J. Wang, “Multiformity of inherent randomicity and visitation density in n-symbolic dynamics,” Chaos, Solitons and Fractals, Vol. 33, No. 2, pp. 685–694, 2007.

has been cited by the following article:

  • TITLE: Applications of Data Mining Theory in Electrical Engineering

    AUTHORS: Yagang ZHANG, Jing MA, Jinfang ZHANG, Zengping WANG

    KEYWORDS: Fault Analysis, Data Mining Theory, Classification, Electrical Engineering

    JOURNAL NAME: Engineering, Vol.1 No.3, November 25, 2009

    ABSTRACT: In this paper, we adopt a novel applied approach to fault analysis based on data mining theory. In our researches, global information will be introduced into the electric power system, we are using mainly cluster analysis technology of data mining theory to resolve quickly and exactly detection of fault components and fault sections, and finally accomplish fault analysis. The main technical contributions and innovations in this paper include, introducing global information into electrical engineering, developing a new application to fault analysis in electrical engineering. Data mining theory is defined as the process of automatically extracting valid, novel, potentially useful and ultimately comprehensive information from large databases. It has been widely utilized in both academic and applied scientific researches in which the data sets are generated by experiments. Data mining theory will contribute a lot in the study of electrical engineering.