[1] SHAO J, HE X, BOHM C, et al. Synchronization-inspired partitioning and hierarchical clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(4):893-905.
[2] HUANG J, SUN H, SONG Q, et al. Revealing density-based clustering structure from the core-connected tree of a network[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(8):1876-1889.
[3] SHENTAL N, BAR-HILLEL A, HERTZ T, et al. Gaussian mixture models with equivalence constraints[M]//BASU S, DAVIDSON I, WAGSTAFF K. Constrained clustering:advances in algorithms, theory, and applications. Boca Raton:CRC Press, 2008:33-58.
[4] MORSIER D F, TUIA D, BORGEAUD M, et al. Cluster validity measure and merging system for hierarchical clustering considering outliers[J]. Pattern Recognition, 2015, 48(4):1478-1489.
[5] PARIKH M, VARMA T. Survey on different grid based clustering algorithms[J]. International Journal, 2014, 2(2):427-430.
[6] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496.
[7] CHEN M, LI L, WANG B, CHENG J, et al. Effectively clustering by finding density backbone based-on kNN[J]. Pattern Recognition, 2016, 60:486-498.
[8] 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, 354:19-40.
[9] XU J, WANG G, DENG W. DenPEHC:density peak based efficient hierarchical clustering[J]. Information Sciences, 2016, 373:200-218.
[10] RUIZ C, SPILIOPOULOU M, MENASALVAS E. User constraints over data streams[C]//17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases. Berlin:ECML/PKDD, 2006:117-226.
[11] RUIZ C, SPILIOPOULOU M, MENASALVAS E. Density-based semi-supervised clustering[J]. Data Mining and Knowledge Discovery, 2010, 21(3):345-370.
[12] WAGSTAFF K, CARDIE C, ROGERS S, et al. Constrained K-means clustering with background knowledge[C]//Eighteenth International Conference on Machine Learning. San Francisco:Morgan Kaufmann Publishers Inc, 2001:577-584.
[13] BILENKO M, BASU S, MOONEY R J. Integrating constraints and metric learning in semi-supervised clustering[C]//The Twenty-first International Conference on Machine Learning. Banff:ICML, 2004:11.
[14] Rangapuram S S, Hein M. Constrained 1-Spectral Clustering[J]. Computer Science, 2015:1143-1151.
[15] AZIMI J, FERN X. Adaptive cluster ensemble selection[C]//International Joint Conference on Artifical Intelligence. Pasadena:Morgan Kaufmann Publishers Inc, 2009:992-997.
[16] 唐伟, 周志华. 基于Bagging的选择性聚类集成[J]. 软件学报, 2005, 16(4):496-502 TANG Wei, ZHOU Zhi-Hua. Bagging-based selective clusterer ensemble[J]. Journal of Software, 2005, 16(4):496-502
[17] HANSEN L K, SALAMON P. Neural network ensemble[J]. IEEE Computer Society, 1990, 12(10):993-1001.
[18] RAND W M. Objective criteria for the evaluation of clustering methods[J]. Journal of the American Statistical Association, 1971, 66(336):846-850.
[19] HALKIDI M, GUNOPULOS D, KUMAR N. A framework for semi-supervised learning based on subjective and objective clustering criteria[C]//Fifth IEEE International Conference on Data Mining. Houston:IEEE, 2005:637-640.
[20] BACHE K, LICHMAN M. UCI machine learning repository[EB/OL].[2017-06-18] . https://archive.ics.uci.edu/ml/index.php |