Abstract
Fuzzy clustering analysis algorithm has good ability to solve fuzzy problems. Rough clustering analysis has good ability to solve these problems its prior knowledge is uncertain. But in the real world, there are many problems that not only are fuzzy but also are rough and uncertain; the paper combines the idea of these two algorithms. In order to improve correction of clustering, it imports attributes reduction algorithm to get importance of each attribute, and dynamically changes attribute weight by the importance. The new algorithm firstly computes fuzzy membership degree of every object and then estimates the object that belongs to lower approximation or upper approximation of one cluster. In the analysis process, the paper provides a new way to get the cluster centers, combining fuzzy and rough theory. From experiments of four UCI data sets, it is proved that the new algorithm is better effective.
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Acknowledgments
Young Teachers’ Basic Ability Improving Project of Guangxi Education Hall under Grant No. 2018KY0321; The National Natural Science Foundation of China (61462008, 61751213, 61866004); The Key projects of Guangxi Natural Science Foundation (2018GXNSFDA294001,2018GXNSFDA281009); The Natural Science Foundation of Guangxi (2017GXNSFAA198365); 2015 Innovation Team Project of Guangxi University of Science and Technology (gxkjdx201504); Scientific Research and Technology Development Project of Liuzhou (2016C050205); Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics under Grant No. GIIP201508; Young Teachers’ Basic Ability Improving Project of Guangxi Education Hall under Grant No. KY2016YB252; Natural Science Foundation of Guangxi University of Science and Technology under Grant No. 174523.
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Ouyang, H., Wang, Z.W., Huang, Z.J., Hu, W.P. (2019). Fuzzy Rough Clustering Analysis Algorithm Based on Attribute Reduction. In: Quinto, E., Ida, N., Jiang, M., Louis, A. (eds) The Proceedings of the International Conference on Sensing and Imaging, 2018. ICSI 2018. Lecture Notes in Electrical Engineering, vol 606. Springer, Cham. https://doi.org/10.1007/978-3-030-30825-4_1
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DOI: https://doi.org/10.1007/978-3-030-30825-4_1
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