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3332                                Journal of Software  软件学报 Vol.31, No.11, November 2020

                 [4]    Wang  L, Shao  Y.  Crack fault  classification for planetary gearbox  based  on feature selection technique  and  K-means clustering
                     method. Chinese Journal of Mechanical Engineering, 2018,31:Article No.4. [doi: 10.1186/s10033-018-0202-0]
                 [5]    Slimen Y, Allio S, Jacques J. Model-based co-clustering for functional data. Neurocomputing, 2018,291:97−108.
                 [6]    Bai X. Similarity Measures in Cluster Analysis and Its Applications. Beijing: Beijing Jiaotong University, 2012 (in Chinese).
                 [7]    Shi QY,  Liang  JY, Zhao  XW. A clustering ensemble algorithm  for  incomplete mixed  data.  Journal  of Computer Research  and
                     Development, 2016,53(9):1979−1989 (in Chinese with English abstract).
                 [8]    Zhao W, Deng C, Ngo C. k-means: A revisit. Neurocomputing, 2018,291:195−206.
                 [9]    Ienco D, Bordogna G. Fuzzy extensions of the DBScan clustering algorithm. Soft Computing, 2018,22(5):1719−1730.
                [10]    Rodríguez A, Laio A. Clustering by fast search and find of density peaks. Science, 2014,344(6191):1492−1496.
                [11]    Gong  SF, Zhang YF. EDDPC: An efficient  distributed  density  peaks clustering algorithm.  Journal  of  Computer Research and
                     Development, 2016,53(6):1400−1409 (in Chinese with English abstract).
                [12]    Liu R, Wang H, Yu X. Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Information Sciences,
                     2018,450:200−226.
                [13]    Mehmood R, Zhang G, Bie R, Dawood H. Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing,
                     2016,208:210−217.
                [14]    Zhou L, Pei C. Delta-distance based clustering with a divide-and-conquer strategy: 3DC clustering. Pattern Recognition Letters,
                     2016,73:52−59.
                [15]    Ding S, Du M, Sun T, Xu X, Xue Y. An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy
                     neighborhood. Knowledge-Based Systems, 2017,133:294−313.
                [16]    Bai L, Cheng X, Liang J, Shen H, Guo Y. Fast density clustering strategies based on the k-means algorithm. Pattern Recognition,
                     2017,71:375−386.
                [17]    Shi Y, Chen Z, Qi Z, Meng F, Cui L. A novel clustering-based image segmentation via density peaks algorithm with mid-level
                     feature. Neural Computing and Applications, 2017,28(1):29−39.
                [18]    Zhang Y, Xia Y,  Liu Y, Wang W. Clustering sentences with density peaks for multi-document summarization. In: Proc. of the
                     NAACL HLT 2015. Denver: ACL, 2015. 1262−1267.
                [19]    Chen Y, Lai D, Qi H, Wang J, Du J. A new method to estimate ages of facial image for large database. Multimedia Tools and
                     Applications, 2016,75(5):2877−2895.
                [20]    Du M,  Ding S, Jia  H. Study on density peaks  clustering based on  k-nearest  neighbors and  principal component analysis.
                     Knowledge-based Systems, 2016,99:135−145.
                [21]    Xie J, Gao H, Xie W, Liu X, Grant P. Robust clustering by detecting density peaks and assigning points based on fuzzy weighted
                     K-nearest neighbors. Information Sciences, 2016,354:19−40.
                [22]    Xie JY, Gao HC, Xie WX. K-nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a
                     dataset. Scientia Sinica Informationis, 2016,46(2):258−280 (in Chinese with English abstract).
                [23]    Krumhansl  C.  Concerning the  applicability of geometric  models to similarity data:  The interrelationship between similarity  and
                     spatial density. Psychological Review, 1978,85(5):445−463.
                [24]    Kai M, Zhu Y, Carman M, Zhu Y, Zhou Z. Overcoming key weaknesses of distance-based neighbourhood methods using a data
                     dependent dissimilarity measure. In: Proc. of the KDD 2016. San Francisco: ACM, 2016. 1205−1214.
                [25]    Aryal S, Kai  MT,  Haffari G, Washio T. m p-dissimilarity: A data dependent dissimilarity  measure.  In: Proc. of the ICDM 2014.
                     Shenzhen: IEEE, 2014. 707−712.
                [26]    Chen B, Ting K, Washio T, Haffari G. Half-Space mass: A maximally robust and efficient data depth method. Machine Learning,
                     2015,100(2-3):677−699.

                 附中文参考文献:
                  [6]  白雪.聚类分析中的相似性度量及其应用研究.北京:北京交通大学,2012.
                  [7]  史倩玉,梁吉业,赵兴旺.一种不完备混合数据集成聚类算法.计算机研究与发展,2016,53(9):1979−1989.
                 [11]  巩树凤,张岩峰.EDDPC:一种高效的分布式密度中心聚类算法.计算机研究与发展,2016,53(6):1400−1409.
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