[1] CHEN Lifei.Research and application of high-dimensional data clustering method[D].Xiamen:Xiamen University,2008.(in Chinese) 陈黎飞.高维数据的聚类方法研究与应用[D].厦门:厦门大学,2008. [2] XIA Jiazhi,ZHANG Yawei,ZHANG Jian.Local correlation visual analysis based on subspace clustering[J].Journal of Computer-Aided Design and Computer Graphics,2016,28(11):1855-1862.(in Chinese) 夏佳志,张亚伟,张健.一种基于子空间聚类的局部相关性可视分析方法[J].计算机辅助设计与图形学学报,2016,28(11):1855-1862. [3] HAN R T N J.Efficient and effective clustering methods for spatial data mining[C]//Proceedings of the20thIEEE International Conference on Very Large Data Bases.Washington D.C.,USA:IEEE Press,1994:144-155. [4] WANG Qian,WANG Cheng,FENG Zhenyuan.Review of K-means clustering algorithm[J].Electronic Design Engineering,2012,20(7):21-24.(in Chinese) 王千,王成,冯振元.K-Means聚类算法研究综述[J].电子设计工程,2012,20(7):21-24. [5] CLEUZIOU G.An extended version of the K-Means method for overlapping clustering[C]//Proceedings of International Conference on Pattern Recognition.Washington D.C.,USA:IEEE Press,2008:1-4. [6] WHANG J J,HOU Y,GLEICH D,et al.Non-exhaustive,overlapping clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(1):427-436. [7] AGGARWAL C,PROCOPIUC C,WOLFJ L,et al.Fast algorithms for projected clustering[C]//Proceedings of ACM SIGMOD International Conference on Management of Data.New York,USA:ACM Press,1999:61-72. [8] CHEN Yuanyuan,LEI Zhang,ZHANG Yi.Subspace clustering using a low-rank constrained autoencoder[J].Information Sciences,2017,424:27-38. [9] MULLER E,GUNNEMANN S,ASSENT I,et al.Evaluating clustering in subspace projections of high dimensional data[J].Proceedings of the Very Large Data Bases Endowment,2009,2(1):1270-1281. [10] JING L,NG M K,HUANG J Z.An entropy weighting K-Means algorithm for subspace clustering of high-dimensional sparse data[J].IEEE Transactions on Knowledge and Data Engineering,2007,19(8):1026-1041. [11] GUNNEMANN S,FARBER I,RAUBACH S,et al.Spectral subspace clustering for graphs with feature vectors[C]//Proceedings of International Conference on Data Mining.[S.1.]:IEEE Computer Society,2013:123-132. [12] GAN Yanglan.Research on subspace clustering algorithm for high-dimensional data[D].Hefei:Hefei University of Technology,2007.(in Chinese)甘杨兰.面向高维数据的子空间聚类算法研究[D].合肥:合肥工业大学,2007. [13] LIU Haijiao,MA Huifang,CHANG Yang,et al.Target community detection with user's preference and attribute subspace[J].IEEE Access,2019,7:46583-46594. [14] HUANG J,NG M,RONG H,et al.Automated variable weighting in K-Means type clustering[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(5):657-668. [15] CHAN Y,CHING W,NG M,et al.An optimization algorithm for clustering using weighted dissimilarity measures[J].Pattern Recognition,2004,37(5):943-952. [16] FRIGUI H,NASRAOUUI O.Unsupervised learning of prototypes and attribute weights[J].Pattern Recognition,2004,37(3):567-581. [17] ARTHUR D.K-Means++:the advantages of careful seeding[C]//Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms.New York,USA:ACM Press,2007:223-234. [18] STREHL A,GHOSH J.Cluster ensembles-a knowledge reuse framework for combining multiple partitions[J].Journal of Machine Learning Research,2002,3(3):583-617. [19] YANG J,LESKOVEC J.Overlapping community detection at scale:a nonnegative matrix factorization approach[C]//Proceedings of the 6th ACM International Conference on Web Search and Data Mining.New York,USA:ACM Press,2013:324-331. [20] BANERJEE A,KRUMPELMAN C,GHOSH J,et al.Model-based overlapping clustering[C]//Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining.New York,USA:ACM Press,2005:116-128. |