Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Data Mining for Uncertain Data Based on Difference Degree of Concept Lattice

  • Qian Wang (School of Computer Science and Technology, Zhoukou Normal University) ;
  • Shi Dong (School of Computer Science and Technology, Zhoukou Normal University) ;
  • Hamad Naeem (School of Computer Science and Technology, Zhoukou Normal University)
  • Received : 2022.03.10
  • Accepted : 2022.11.01
  • Published : 2024.06.30
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Abstract

Along with the rapid development of the database technology, as well as the widespread application of the database management systems are more and more large. Now the data mining technology has already been applied in scientific research, financial investment, market marketing, insurance and medical health and so on, and obtains widespread application. We discuss data mining technology and analyze the questions of it. Therefore, the research in a new data mining method has important significance. Some literatures did not consider the differences between attributes, leading to redundancy when constructing concept lattices. The paper proposes a new method of uncertain data mining based on the concept lattice of connotation difference degree (c_diff). The method defines the two rules. The construction of a concept lattice can be accelerated by excluding attributes with poor discriminative power from the process. There is also a new technique of calculating c_diff, which does not scan the full database on each layer, therefore reducing the number of database scans. The experimental outcomes present that the proposed method can save considerable time and improve the accuracy of the data mining compared with U-Apriori algorithm.

Keywords

Acknowledgement

This paper is supported by Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (SKLNST-2020-2-01), Key Scientific Research Projects of Colleges and Universities in Henan Province (Grant No. 23A520054).

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