Page 16 - 《软件学报》2020年第11期
P. 16
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.