JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2019, Vol. 54 ›› Issue (9): 9-14.doi: 10.6040/j.issn.1671-9352.0.2019.189

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Quantitative characteristics and applications of P-information hidden mining

Xiu-mei HAO(),Ning-ning LI   

  1. School of Mathematic and Quantitative Economics, Shandong University of Finance and Economics, Jinan 250100, Shandong, China
  • Received:2019-04-25 Online:2019-09-20 Published:2019-07-30
  • Supported by:
    山东省研究生教育创新计划项目(SDYY15061)

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Abstract:

The classical set has static characteristics. With the rapid development of science and technology, massive data updates very quickly. Therefore using the classical set to study data mining problems has been limited. It is necessary to involve dynamic characteristics into ordinary set and replace "static" by "dynamic". Thus P-set has the above characteristics. By using P-set and its attribute migration, the granularity and mining degree of internal and external k-order P-information are proposed; also the quantitative relationship between attribute migration and information mining is discussed. The migration information chain theorem and the minimum and maximum mining degree theorem are given. P-information is a new theory and method for dynamic information system knowledge mining. Finally, the application of P-information in data mining is given.

Key words: P-information, quantitative characteristics, attribute transfer, mining theorem, hidden

CLC Number: 

  • O159
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