暂无分享,去创建一个
[1] Pasi Fränti,et al. Iterative shrinking method for clustering problems , 2006, Pattern Recognit..
[2] Yoshua Bengio,et al. Convergence Properties of the K-Means Algorithms , 1994, NIPS.
[3] T. Moon. The expectation-maximization algorithm , 1996, IEEE Signal Process. Mag..
[4] Victoria J. Hodge,et al. A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.
[5] Andrew W. Moore,et al. X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.
[6] Fionn Murtagh,et al. Algorithms for hierarchical clustering: an overview , 2012, WIREs Data Mining Knowl. Discov..
[7] Peter Meer,et al. Semi-Supervised Kernel Mean Shift Clustering , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] Tomi Kinnunen,et al. Improving K-Means by Outlier Removal , 2005, SCIA.
[9] Ting Su,et al. In search of deterministic methods for initializing K-means and Gaussian mixture clustering , 2007, Intell. Data Anal..
[10] W. Rudin. Principles of mathematical analysis , 1964 .
[11] Jiang-She Zhang,et al. Robust clustering by pruning outliers , 2003, IEEE Trans. Syst. Man Cybern. Part B.
[12] Sean Hughes,et al. Clustering by Fast Search and Find of Density Peaks , 2016 .
[13] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[14] Ali S. Hadi,et al. Finding Groups in Data: An Introduction to Chster Analysis , 1991 .
[15] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[16] Zhou Wang,et al. Complex Wavelet Structural Similarity: A New Image Similarity Index , 2009, IEEE Transactions on Image Processing.
[17] Argyris Kalogeratos,et al. Dip-means: an incremental clustering method for estimating the number of clusters , 2012, NIPS.
[18] Sameer A. Nene,et al. Columbia Object Image Library (COIL100) , 1996 .
[19] Jiye Liang,et al. An initialization method for the K-Means algorithm using neighborhood model , 2009, Comput. Math. Appl..
[20] Aristidis Likas,et al. The MinMax k-Means clustering algorithm , 2014, Pattern Recognit..
[21] Philip S. Yu,et al. Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.
[22] Pavel Berkhin,et al. A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.
[23] J. Hartigan,et al. The Dip Test of Unimodality , 1985 .
[24] Rui Xu,et al. Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.
[25] Andy Harter,et al. Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.
[26] Lorenzo Rosasco,et al. Learning Manifolds with K-Means and K-Flats , 2012, NIPS.
[27] Andrew Y. Ng,et al. The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization , 2011, ICML.
[28] Simone Santini,et al. Similarity Measures , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[29] Patricio A. Vela,et al. A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm , 2012, Expert Syst. Appl..
[30] H. Sebastian Seung,et al. The Manifold Ways of Perception , 2000, Science.
[31] Fevzi Alimo. Methods of Combining Multiple Classiiers Based on Diierent Representations for Pen-based Handwritten Digit Recognition , 1996 .
[32] D. Rajan. Probability, Random Variables, and Stochastic Processes , 2017 .
[33] S. Sheather. Density Estimation , 2004 .
[34] Kamesh Munagala,et al. Local Search Heuristics for k-Median and Facility Location Problems , 2004, SIAM J. Comput..
[35] L. Wasserman,et al. A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion , 1995 .
[36] Pasi Fränti,et al. A Dynamic local search algorithm for the clustering problem , 2002 .
[37] Shokri Z. Selim,et al. K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Greg Hamerly,et al. Learning the k in k-means , 2003, NIPS.
[39] Christos Boutsidis,et al. Randomized Dimensionality Reduction for $k$ -Means Clustering , 2011, IEEE Transactions on Information Theory.
[40] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[41] Brendan J. Frey,et al. Non-metric affinity propagation for unsupervised image categorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[42] Fengfu Li,et al. A new manifold distance measure for visual object categorization , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).
[43] Yuanyuan Li,et al. Feature selection based on sensitivity analysis of fuzzy ISODATA , 2012, Neurocomputing.
[44] Hassan A. Kingravi,et al. Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm , 2014, ArXiv.
[45] Hans-Peter Kriegel,et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.
[46] Anil K. Jain. Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..
[47] Hae-Sang Park,et al. A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..
[48] Longbing Cao,et al. A novel graph-based k-means for nonlinear manifold clustering and representative selection , 2014, Neurocomputing.