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  2019, Vol. 32 Issue (11): 1032-1041    DOI: 10.16451/j.cnki.issn1003-6059.201911008
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Automatic Determination of Clustering Center for Clustering by Fast Search and Find of Density Peaks
WANG Wanliang1, WU Fei1, LÜ Chuang1
1.College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023

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Abstract  Clustering center cannot be automatically selected by the algorithm of fast search and find of density peaks. To solve the problem, automatic determination of clustering centers for clustering by fast search and find of density peaks is proposed. Firstly, density and distance are normalized for the problem of uneven distribution of variables, and then the upper limit of normalized density threshold is determined by Chebyshev inequality. Standard deviation is utilized to determine the upper limit of normalized distance threshold. Finally, the upper limit of decision threshold is determined according to the decision function. Two determinants are considered comprehensively to avoid the omission of the central point selection and realize the automatic determination of the cluster centers. The experiment shows that the adaptive selection of the clustering centers of the proposed algorithm is effective with good robustness and validity.
Key wordsDensity Peak      Clustering Algorithm      Clustering Center      Chebyshev Inequality     
Received: 28 March 2019     
ZTFLH: TP 391  
Fund:Supported by National Natural Science Foundation of China(No.61873240)
Corresponding Authors: WANG Wanliang, Ph.D., professor. His research interests include deep learning, artificial intelligence and big data.   
About author:: WU Fei, master student. Her research interests include big data and data mining.LÜ Chuang, master student. His research interests include big data and data mining.
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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201911008      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I11/1032
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