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A data reduction method in formal fuzzy contexts

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Abstract

As a basic operation in data mining and knowledge discovery, data reduction can reduce the volume of the data and simplify the representation of knowledge. In this paper we propose a method of attribute reduction in a formal fuzzy context based on the notion of “one-sided fuzzy concept”. According to the importance of attributes, we classify the attributes into three types: core attributes, relatively necessary attributes and unnecessary attributes, which are also referred to the attribute characteristics. We propose judgment theorems and the corresponding algorithms for computing the three types of attribute sets. Moreover, a straightforward attribute reduction method by virtue of attribute characteristics is formulated. We show that the computation of formal concepts on the reduced data set is made more efficient, and yet produces the same lattice structure and conceptual hierarchy as the ones derived from the original formal context.

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Acknowledgments

The authors are very indebted to the anonymous referees for their critical comments and suggestions for the improvement of this paper. This work was supported by Grants from the National Natural Science Foundation of China (Nos. 61173181, 61272021, 61363056, 61573321), the National Social Science Foundation of China (No. 14XXW004), the Humanities and Social Science funds Project of Ministry of Education of China (No. 11XJJAZH001), the Zhejiang Provincial Nature Science Foundation of China (No. LZ12F03002), and the open project of Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province (No. OBDMA201504).

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Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, 266580, Qingdao, Shandong, China

    Kewen Li & Ming-Wen Shao

  2. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, 316022, Zhoushan, Zhejiang, China

    Wei-Zhi Wu

  3. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, 316022, Zhoushan, China

    Wei-Zhi Wu

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  1. Kewen Li
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  2. Ming-Wen Shao
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Correspondence to Ming-Wen Shao.

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Li, K., Shao, MW. & Wu, WZ. A data reduction method in formal fuzzy contexts . Int. J. Mach. Learn. & Cyber. 8, 1145–1155 (2017). https://doi.org/10.1007/s13042-015-0485-8

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  • DOI: https://doi.org/10.1007/s13042-015-0485-8

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