吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 1604-1611.doi: 10.13229/j.cnki.jdxbgxb201705037

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Clustering method for uncertain data based on spectral decomposition

LI Jia-fei1, 2, SUN Xiao-yu1, 2   

  1. 1.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    2.College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2016-07-05 Online:2017-09-20 Published:2017-09-20

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Abstract: A clustering method for uncertain data based on spectral decomposition was proposed. The method was applied to explore the true covariance structure of data records behind the uncertain representation under the natural potential association of the data. The data analysis method based on spectral decomposition can get the sharpening data according to the covariance structure. Then, clustering analysis of the sharpening data is carried out. The comparison experiment results show that, using the proposed method, the clustering quality improves significantly; the results of root mean square error and CH index are all better than that obtained using the traditional method.

Key words: artificial intelligence, uncertain data, spectral decomposition, clustering, data sharpening, covariance structure

CLC Number: 

  • TP391
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