计算机科学 ›› 2018, Vol. 45 ›› Issue (2): 125-129.doi: 10.11896/j.issn.1002-137X.2018.02.022
刘奕志,程汝峰,梁永全
LIU Yi-zhi, CHENG Ru-feng and LIANG Yong-quan
摘要： 基于加权K近邻的密度峰值发现算法(FKNN-DPC)是一种简单、高效的聚类算法,能够自动发现簇中心,并采用加权K近邻的思想快速、准确地完成对非簇中心样本的分配,在各种规模、任意维度、任意形状的数据集上都能得到高质量的聚类结果,但其样本分配策略中的权重仅考虑了样本间的欧氏距离。文中提出了一种基于共享近邻的相似度度量方式,并以此相似度改进样本分配策略,使得样本的分配更符合真实的簇归属情况,从而提高聚类质量。在UCI真实数据集上进行实验,并将所提算法与K-means,DBSCAN,AP,DPC,FKNN-DPC等算法进行对比,验证了其有效性。
| [1] JAIN A K,DUBES R C.Algorithms for clustering data[M].Upper Saddle Riever:Prentice Hall,1988. [2] BERKHIN P.A Survey of Clustering Data Mining Techniques[J].Grouping Multidimensional Data,2006,43(1):25-71. [3] XU R,ND W D.Survey of clustering algorithms[J].IEEETransactions on Neural Networks,2005,16(3):645-678. [4] GELBARD R,GOLDMAN O,SPIEGLER I.Investigating diversity of clustering methods:An empirical comparison[J].Data & Knowledge Engineering,2007,63(1):155-166. [5] SUN J G,LIU J,ZHAO L Y.Clustering Algorithms Research[J].Journal of Software,2008,19(1):48-61.(in Chinese) 孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008,19(1):48-61. [6] MACQUEEN J.Some Methods for Classification and Analysisof MultiVariate Observations[C]∥Proc.of Berkeley Symposiumon Mathematical Statistics and Probability.1967:281-297. [7] GUHA S,RASTOGI R,SHIM K,et al.CURE:An EfficientClustering Algorithm for Large Databases[J].Information Systems,1998,26(1):35-58. [8] KARYPIS G,HAN E H,KUMAR V.CHAMELEON A hierarchical clustering algorithm using dynamic modeling[J].Compu-ter,1999,32(8):68-75. [9] ESTER M,KRIEGEL H P,XU X.A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise[C]∥ International Conference on Knowledge Discovery and Data Mining.1996:226-231. [10] FAN J.OPE-HCA:an optimal probabilistic estimation approach for hierarchical clustering algorithm[J/OL].Neural Computing &Applications,2015:1-11.https://link.springer.com/article/10.1007%2Fs00521-015-1998-5. [11] AGRAWAL R,GEHRKE J,GUNOPULOS D,et al.Automatic subspace clustering of high dimensional data for data mining applications[M]∥ACM SIGMOD Record.ACM,1998:94-105. [12] FREY B J,DUECK D.Clustering by passing messages between data points[J].Science,2007,315(5814):972-976. [13] RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496. [14] DU M,DING S,JIA H.Study on density peaks clustering based on k-nearest neighbors and principal component analysis[J].Knowledge-Based Systems,2016,99:135-145. [15] MEHMOOD R,ZHANG G,BIE R,et al.Clustering by fastsearch and find of density peaks via heat diffusion[J].Neurocomputing,2016,208(C):210-217. [16] XIE J Y,GAO H C,XIE W X.K-nearest neighbors optimized clustering algorithm by fast search and nding the density peaks of a dataset[J].Scientia Sinica Informationis,2016,46(2):258-280.(in Chinese) 谢娟英,高红超,谢维信.K 近邻优化的密度峰值快速搜索聚类算法[J].中国科学:信息科学,2016,46(2):258-280. [17] XIE J,GAO H,XIE W,et al.Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors[J].Information Sciences,2016,354(C):19-40. [18] ZHANG W,LI J.Extended fast search clustering algorithm:widely density clusters,no density peaks[J].arXiv preprint arXiv:1505.05610,2015. [19] LICHMAN M.UCI Machine Learning Repository .ht-tp://archive.ics.uci.edu/ml. [20] VINH N X,EPPS J,BAILEY J.Information Theoretic Mea-sures for Clusterings Comparison:Variants,Properties,Norma-lization and Correction for Chance[C]∥International Confe-rence on Machine Learning(ICML 2009).Montreal,Quebec,Canada,2009:2837-2854. |
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