14.2.17 K-Means Clustering

Chapter Contents (Back)
Classification. Pattern Recognition. K-Means. K-Means clustering generates a specific number of disjoint, flat (non-hierarchical) clusters. The K-Means method is numerical, unsupervised, non-deterministic and iterative. ISODATA is similar to K-Means, except ISODATA does not assume a given number of clusters.

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A k-means clustering algorithm,
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K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality,
PAMI(6), No. 1, January 1984, pp. 81-87.
See also Fuzzy C-Means: Optimality of solutions and effective termination of the algorithm. BibRef 8401

Navarro, A., Allen, C.R.,
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OptEng(36), No. 1, 1997, pp. 31-38. Journal ref. may not be right. BibRef 9700

Navarro, A.,
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IEEE DOI 9812
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Chen, C.W.[Chang Wen], Luo, J.B.[Jie-Bo], Parker, K.J., Huang, T.S.,
A knowledge-based approach to volumetric medical image segmentation,
ICIP94(III: 493-497).
IEEE DOI 9411
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Tyree, E.W., Long, J.A.,
A Monte Carlo Evaluation of the Moving Method, K-means and Self-Organising Neural Networks,
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Su, M.C.[Mu-Chun], Chou, C.H.[Chien-Hsing],
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry,
PAMI(23), No. 6, June 2001, pp. 674-680.
IEEE DOI 0106
A non-metric distance based on point symmetry. Applied to face detection. BibRef

Peña, J.M., Lozano, J.A., Larrañaga, P.,
An empirical comparison of four initialization methods for the K-Means algorithm,
PRL(20), No. 10, October 1999, pp. 1027-1040. 9911
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Ng, M.K.[Michael K.],
A note on constrained k-means algorithms,
PR(33), No. 3, March 2000, pp. 515-519.
Elsevier DOI 0001
BibRef

Kanungo, T.[Tapas], Mount, D.M.[David M.], Netanyahu, N.S.[Nathan S.], Piatko, C.D.[Christine D.], Silverman, R.[Ruth], Wu, A.Y.[Angela Y.],
An Efficient k-Means Clustering Algorithm: Analysis and Implementation,
PAMI(24), No. 7, July 2002, pp. 881-892.
IEEE Abstract. 0207
BibRef
Earlier:
The Analysis of a Simple k-means Clustering Algorithm,
UMD--TR4098, January 2000.
WWW Link. Determine the k cluster centers. Simple implementation of Lloyd's algorithm (
See also Least Squares Quantization in PCM. ). BibRef

Mount, D.M.[David M.], Netanyahu, N.S.[Nathan S.], Piatko, C.D.[Christine D.], Silverman, R.[Ruth], Wu, A.Y.[Angela Y.],
Quantile Approximation for Robust Statistical Estimation and k-enclosing Problems,
UMD--TR3941, October 1998. least median-of-squares regression.
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Clausi, D.A.,
K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation,
PR(35), No. 9, September 2002, pp. 1959-1972.
Elsevier DOI 0206
BibRef

Likas, A.C.[Aristidis C.], Vlassis, N.[Nikos], Verbeek, J.J.[Jakob J.],
The global k-means clustering algorithm,
PR(36), No. 2, February 2003, pp. 451-461.
Elsevier DOI 0211
BibRef

Cheung, Y.M.[Yiu-Ming],
k*-Means: A new generalized k-means clustering algorithm,
PRL(24), No. 15, November 2003, pp. 2883-2893.
Elsevier DOI 0308
Clustering without a priori number of clusters. BibRef

Tarsitano, A.[Agostino],
A computational study of several relocation methods for k-means algorithms,
PR(36), No. 12, December 2003, pp. 2955-2966.
Elsevier DOI 0310
BibRef

Khan, S.S.[Shehroz S.], Ahmad, A.[Amir],
Cluster center initialization algorithm for K-means clustering,
PRL(25), No. 11, August 2004, pp. 1293-1302.
Elsevier DOI 0409
BibRef

Maliatski, B., Yadid-Pecht, O.,
Hardware-Driven Adaptive K-Means Clustering for Real-Time Video Imaging,
CirSysVideo(15), No. 1, January 2005, pp. 164-166.
IEEE Abstract. 0501
BibRef

Chan, E.Y.[Elaine Y.], Ching, W.K.[Wai Ki], Ng, M.K.[Michael K.], Huang, J.Z.[Joshua Z.],
An optimization algorithm for clustering using weighted dissimilarity measures,
PR(37), No. 5, May 2004, pp. 943-952.
Elsevier DOI 0405
BibRef

San, O., Huynh, V., Nakamori, Y.,
An Alternative Extension of the k-Means Algorithm for Clustering Categorical Data,
JAMCS(14), No. 2, 2004, pp. 241-247. i-Mode. BibRef 0400

Huang, J.Z.[Joshua Zhexue], Ng, M.K.[Michael K.], Rong, H.Q.[Hong-Qiang], Li, Z.C.[Zi-Chen],
Automated Variable Weighting in k-Means Type Clustering,
PAMI(27), No. 5, May 2005, pp. 657-668.
IEEE Abstract. 0501
Automatically update variable weights based on the current partition. BibRef

Yu, J.[Jian],
General C-Means Clustering Model,
PAMI(27), No. 8, August 2005, pp. 1197-1211.
IEEE Abstract. 0506
BibRef
Earlier:
General C-Means Clustering Model and Its Application,
CVPR03(II: 122-127).
IEEE DOI 0307
BibRef

Charalampidis, D.,
A Modified K-Means Algorithm for Circular Invariant Clustering,
PAMI(27), No. 12, December 2005, pp. 1856-1865.
IEEE DOI 0512
Vector based for circular invariant clustering. BibRef

Chung, K.L.[Kuo-Liang], Lin, K.S.[Keng-Sheng],
An efficient line symmetry-based K-means algorithm,
PRL(27), No. 7, May 2006, pp. 765-772.
Elsevier DOI Clustering; Point symmetry; Line symmetry 0604
BibRef

Chung, K.L.[Kuo-Liang], Lin, J.S.[Jhin-Sian],
Faster and more robust point symmetry-based K-means algorithm,
PR(40), No. 2, February 2007, pp. 410-422.
Elsevier DOI 0611
Inter-cluster; Intra-cluster; Point symmetry; Robustness; Speedup BibRef

Laszlo, M., Mukherjee, S.,
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering,
PAMI(28), No. 4, April 2006, pp. 533-543.
IEEE DOI 0604
BibRef

Peters, G.[Georg],
Some refinements of rough k-means clustering,
PR(39), No. 8, August 2006, pp. 1481-1491.
Elsevier DOI 0606
Cluster algorithms; Soft computing; Data analysis; Forest data; Bioinformatics data BibRef

Redmond, S.J.[Stephen J.], Heneghan, C.[Conor],
A method for initialising the K-means clustering algorithm using kd-trees,
PRL(28), No. 8, 1 June 2007, pp. 965-973.
Elsevier DOI 0704
Clustering; K-means algorithm; Kd-tree; Initialisation, Density estimation BibRef

Laszlo, M.[Michael], Mukherjee, S.[Sumitra],
A genetic algorithm that exchanges neighboring centers for k-means clustering,
PRL(28), No. 16, December 2007, pp. 2359-2366.
Elsevier DOI 0711
k-means algorithm; Clustering; Genetic algorithms; Optimal partition; Center selection BibRef

Saegusa, T.[Takashi], Maruyama, T.[Tsutomu],
An FPGA implementation of real-time K-means clustering for color images,
RealTimeIP(2), No. 4, December 2007, pp. 309-318.
Springer DOI 0712
BibRef
Earlier: A2, Only:
Real-time K-Means Clustering for Color Images on Reconfigurable Hardware,
ICPR06(II: 816-819).
IEEE DOI 0609
BibRef

Li, M.Q.[Min-Qiang], Tian, J.[Jin], Chen, F.Z.[Fu-Zan],
Improving multiclass pattern recognition with a co-evolutionary RBFNN,
PRL(29), No. 4, 1 March 2008, pp. 392-406.
Elsevier DOI 0711
RBFNN; Co-operative co-evolutionary algorithms; K-means clustering; Multiclass classification BibRef

Lu, J.F., Tang, J.B., Tang, Z.M., Yang, J.Y.,
Hierarchical initialization approach for K-Means clustering,
PRL(29), No. 6, 15 April 2008, pp. 787-795.
Elsevier DOI 0803
K-Means algorithm; K-Means initialization; Voronoi tessellation; Hierarchical technique BibRef

Mignotte, M.,
Segmentation by Fusion of Histogram-Based K-Means Clusters in Different Color Spaces,
IP(17), No. 5, May 2008, pp. 780-787.
IEEE DOI 0804
BibRef

Zalik, K.R.[Krista Rizman],
An efficient k-means clustering algorithm,
PRL(29), No. 9, 1 July 2008, pp. 1385-1391.
Elsevier DOI 0711
Clustering analysis; k-Means; Cluster number; Cost-function; Rival penalized BibRef

Zalik, K.R.[Krista Rizman],
Cluster validity index for estimation of fuzzy clusters of different sizes and densities,
PR(43), No. 10, October 2010, pp. 3374-3390.
Elsevier DOI 1007
Unsupervised classification; Fuzzy clustering; Cluster validity; Fuzzy c-means BibRef

Zalik, K.R.[Krista Rizman], Zalik, B.[Borut],
Validity index for clusters of different sizes and densities,
PRL(32), No. 2, 15 January 2011, pp. 221-234.
Elsevier DOI 1101
Clustering; k-Means clustering; Unsupervised classification; Validity index BibRef

Al Hasan, M.[Mohammad], Chaoji, V.[Vineet], Salem, S.[Saeed], Zaki, M.J.[Mohammed J.],
Robust partitional clustering by outlier and density insensitive seeding,
PRL(30), No. 11, 1 August 2009, pp. 994-1002.
Elsevier DOI 0909
k-Means; Seed selection; Robust initialization; Partitional clustering BibRef

Tsai, C.F.[Chih-Fong], Lin, C.Y.[Chia-Ying],
A triangle area based nearest neighbors approach to intrusion detection,
PR(43), No. 1, January 2010, pp. 222-229.
Elsevier DOI 0909
Intrusion detection; Machine learning; Triangle area; k-means; k-nearest neighbors; Support vector machines For networks, not vision. BibRef

Hua, C.S.[Chun-Sheng], Chen, Q.[Qian], Wu, H.Y.[Hai-Yuan], Wada, T.[Toshikazu],
RK-Means Clustering: K-Means with Reliability,
IEICE(E91-D), No. 1, January 2008, pp. 96-104.
DOI Link 0801
BibRef

Bagirov, A.M.[Adil M.],
Modified global k-means algorithm for minimum sum-of-squares clustering problems,
PR(41), No. 10, October 2008, pp. 3192-3199.
Elsevier DOI 0808
Minimum sum-of-squares clustering; Nonsmooth optimization; k-Means algorithm; Global k-means algorithm BibRef

Li, J.[Jing], Li, X.L.[Xue-Long], Tao, D.C.[Da-Cheng],
KPCA for semantic object extraction in images,
PR(41), No. 10, October 2008, pp. 3244-3250.
Elsevier DOI 0808
Segmentation; KPCA; KMeans; Kernel KMeans; GMM; Kernel GMM BibRef

Lai, J.Z.C.[Jim Z.C.], Liaw, Y.C.[Yi-Ching],
Improvement of the k-means clustering filtering algorithm,
PR(41), No. 12, December 2008, pp. 3677-3681.
Elsevier DOI 0810
k-Means clustering; Nearest-neighbor search; Knowledge discovery BibRef

Lai, J.Z.C.[Jim Z.C.], Huang, T.J.[Tsung-Jen], Liaw, Y.C.[Yi-Ching],
A fast k-means clustering algorithm using cluster center displacement,
PR(42), No. 11, November 2009, pp. 2551-2556.
Elsevier DOI 0907
k-Means clustering; Nearest-neighbor search; Knowledge discovery BibRef

Liaw, Y.C.[Yi-Ching], Leou, M.L.[Maw-Lin], Wu, C.M.[Chien-Min],
Fast exact k nearest neighbors search using an orthogonal search tree,
PR(43), No. 6, June 2010, pp. 2351-2358.
Elsevier DOI 1003
k nearest neighbors; Fast algorithm; Principal axis search tree; Orthonormal basis BibRef

Lai, J.Z.C.[Jim Z.C.], Huang, T.J.[Tsung-Jen],
Fast global k-means clustering using cluster membership and inequality,
PR(43), No. 5, May 2010, pp. 1954-1963.
Elsevier DOI 1003
Global k-means clustering; Nearest-neighbor search; Knowledge discovery BibRef

Liaw, Y.C.[Yi-Ching],
Improvement of the fast exact pairwise-nearest-neighbor algorithm,
PR(42), No. 5, May 2009, pp. 867-870.
Elsevier DOI 0902
Data clustering; Pairwise-nearest-neighbor; Fast search algorithm BibRef

Chen, G.L.[Guang-Liang], Lerman, G.[Gilad],
Spectral Curvature Clustering (SCC),
IJCV(81), No. 3, March 2009, pp. xx-yy.
Springer DOI 0902
BibRef
And:
Motion segmentation by SCC on the hopkins 155 database,
WDV09(759-764).
IEEE DOI 0910
Linear storage and takes linear running time. Iterative sampling to improve sampling, reduce outliers.
See also Tensor Decomposition for Geometric Grouping and Segmentation, A. BibRef

Wang, X.[Xu], Atev, S.[Stefan], Wright, J.[John], Lerman, G.[Gilad],
Fast Subspace Search via Grassmannian Based Hashing,
ICCV13(2776-2783)
IEEE DOI 1403
Grassmannian Based Hashing; Locality Sensitive Hashing; Subspace Search BibRef

Chen, G.L.[Guang-Liang], Atev, S.[Stefan], Lerman, G.[Gilad],
Kernel Spectral Curvature Clustering (KSCC),
WDV09(765-772).
IEEE DOI 0910
BibRef

Zhang, T.[Teng], Szlam, A.[Arthur], Wang, Y.[Yi], Lerman, G.[Gilad],
Hybrid Linear Modeling via Local Best-Fit Flats,
IJCV(100), No. 3, December 2012, pp. 217-240.
WWW Link. 1210
BibRef
Earlier:
Randomized hybrid linear modeling by local best-fit flats,
CVPR10(1927-1934).
IEEE DOI 1006
BibRef

Zhang, T.[Teng], Szlam, A.[Arthur], Lerman, G.[Gilad],
Median K-Flats for hybrid linear modeling with many outliers,
Subspace09(234-241).
IEEE DOI 0910
BibRef

Chang, D.X.[Dong-Xia], Zhang, X.D.[Xian-Da], Zheng, C.W.[Chang-Wen],
A genetic algorithm with gene rearrangement for K-means clustering,
PR(42), No. 7, July 2009, pp. 1210-1222.
Elsevier DOI 0903
Clustering; Evolutionary computation; Genetic algorithms; K-means algorithm; Remote sensing image BibRef

Chang, D.X.[Dong-Xia], Zhang, X.D.[Xian-Da], Zheng, C.W.[Chang-Wen], Zhang, D.M.[Dao-Ming],
A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem,
PR(43), No. 4, April 2010, pp. 1346-1360.
Elsevier DOI 1002
Clustering; Genetic algorithms; Niching method; Niche migration; Remote sensing image BibRef

Xiong, H., Wu, J., Chen, J.,
K-Means Clustering Versus Validation Measures: A Data-Distribution Perspective,
SMC-B(39), No. 2, April 2009, pp. 318-331.
IEEE DOI 0903
BibRef

Hong, Y., Kwong, S.,
Learning Assignment Order of Instances for the Constrained K-Means Clustering Algorithm,
SMC-B(39), No. 2, April 2009, pp. 568-574.
IEEE DOI 0903
BibRef

Li, Q., Mitianoudis, N., Stathaki, T.,
Spatial kernel K-harmonic means clustering for multi-spectral image segmentation,
IET-IPR(1), No. 2, June 2007, pp. 156-167.
DOI Link 0905
BibRef

Kashef, R.[Rasha], Kamel, M.S.[Mohamed S.],
Enhanced bisecting k-means clustering using intermediate cooperation,
PR(42), No. 11, November 2009, pp. 2557-2569.
Elsevier DOI 0907
Bisecting clustering; Cooperative clustering; Quality measures BibRef

Kashef, R.[Rasha], Kamel, M.S.[Mohamed S.],
Cooperative clustering,
PR(43), No. 6, June 2010, pp. 2315-2329.
Elsevier DOI 1003
Cooperative clustering; Similarity histogram; Cooperative contingency graph BibRef

Chitta, R.[Radha], Murty, M.N.[M. Narasimha],
Two-level k-means clustering algorithm for k-tau relationship establishment and linear-time classification,
PR(43), No. 3, March 2010, pp. 796-804.
Elsevier DOI 1001
Clustering; k-Means; Classification; Linear-time complexity; Support vector machines; k-Nearest neighbor classifier BibRef

Bagirov, A.M.[Adil M.], Ugon, J.[Julien], Webb, D.[Dean],
Fast modified global k-means algorithm for incremental cluster construction,
PR(44), No. 4, April 2011, pp. 866-876.
Elsevier DOI 1101
Minimum sum-of-squares clustering; Nonsmooth optimization; k-means algorithm; Global k-means algorithm BibRef

Bagirov, A.M.[Adil M.], Taheri, S.[Sona], Ugon, J.[Julien],
Nonsmooth DC programming approach to the minimum sum-of-squares clustering problems,
PR(53), No. 1, 2016, pp. 12-24.
Elsevier DOI 1602
Cluster analysis BibRef

Karmitsa, N.[Napsu], Bagirov, A.M.[Adil M.], Taheri, S.[Sona],
Clustering in large data sets with the limited memory bundle method,
PR(83), 2018, pp. 245-259.
Elsevier DOI 1808
Cluster analysis, Nonsmooth optimization, Nonconvex optimization, Bundle methods, Limited memory methods BibRef

Erisoglu, M.[Murat], Calis, N.[Nazif], Sakallioglu, S.[Sadullah],
A new algorithm for initial cluster centers in k-means algorithm,
PRL(32), No. 14, 15 October 2011, pp. 1701-1705.
Elsevier DOI 1110
k-Means algorithm; Initial cluster centers; Rand index; Error percentage; Wilks' lambda test statistic BibRef

de Amorim, R.C.[Renato Cordeiro], Mirkin, B.[Boris],
Minkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering,
PR(45), No. 3, March 2012, pp. 1061-1075.
Elsevier DOI 1111
K-means; Minkowski metric; Feature weights; Noise features; Anomalous cluster BibRef

de Amorim, R.C.[Renato Cordeiro], Shestakov, A.[Andrei], Mirkin, B.[Boris], Makarenkov, V.[Vladimir],
The Minkowski central partition as a pointer to a suitable distance exponent and consensus partitioning,
PR(67), No. 1, 2017, pp. 62-72.
Elsevier DOI 1704
Clustering BibRef

Yu, S.[Shi], Tranchevent, L.[Leon], Liu, X.H.[Xin-Hai], Glanzel, W.[Wolfgang], Suykens, J.A.K.[Johan A.K.], de Moor, B.[Bart], Moreau, Y.[Yves],
Optimized Data Fusion for Kernel k-Means Clustering,
PAMI(34), No. 5, May 2012, pp. 1031-1039.
IEEE DOI 1204
Combine multiple data sources for k-means. Code, Clustering. Code:
HTML Version. BibRef

Liu, F.H.[Fang-Hui], Huang, X.L.[Xiao-Lin], Chen, Y.D.[Yu-Dong], Suykens, J.A.K.[Johan A. K.],
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond,
PAMI(44), No. 10, October 2022, pp. 7128-7148.
IEEE DOI 2209
Kernel, Approximation algorithms, Taxonomy, Scalability, Risk management, Prediction algorithms, Loss measurement, over-parameterized models BibRef

Cleuziou, G.[Guillaume],
Osom: A method for building overlapping topological maps,
PRL(34), No. 3, 1 February 2013, pp. 239-246.
Elsevier DOI 1301
BibRef
Earlier:
An extended version of the k-means method for overlapping clustering,
ICPR08(1-4).
IEEE DOI 0812
Unsupervised Learning; Overlapping clustering; Topological maps; Okm; Som; Osom BibRef

Sarma, T.H.[T. Hitendra], Viswanath, P., Reddy, B.E.[B. Eswara],
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach,
PRL(34), No. 5, 1 April 2013, pp. 564-573.
Elsevier DOI 1303
BibRef
Earlier: A1, A2, Only:
Speeding-Up the K-Means Clustering Method: A Prototype Based Approach,
PReMI09(56-61).
Springer DOI 0912
Unsupervised classification; Kernel k-means clustering method; Leaders clustering method BibRef

Fang, C.L.[Chong-Lun], Jin, W.[Wei], Ma, J.W.[Jin-Wen],
K'-Means algorithms for clustering analysis with frequency sensitive discrepancy metrics,
PRL(34), No. 5, 1 April 2013, pp. 580-586.
Elsevier DOI 1303
Clustering analysis; k-Means; Cluster number; Competitive learning; Discrepancy metric BibRef

Tzortzis, G.[Grigorios], Likas, A.[Aristidis],
The MinMax k-Means clustering algorithm,
PR(47), No. 7, 2014, pp. 2505-2516.
Elsevier DOI 1404
Clustering BibRef

Malinen, M.I.[Mikko I.], Mariescu-Istodor, R.[Radu], Fränti, P.[Pasi],
K-means: Clustering by gradual data transformation,
PR(47), No. 10, 2014, pp. 3376-3386.
Elsevier DOI 1406
BibRef
Earlier: ICIG11(350-355).
IEEE DOI 1109
Or: K-means*? Clustering. BibRef

Malinen, M.I.[Mikko I.], Fränti, P.[Pasi],
Balanced K-Means for Clustering,
SSSPR14(32-41).
Springer DOI 1408
BibRef

Xu, Q.[Qin], Ding, C.[Chris], Liu, J.P.[Jin-Pei], Luo, B.[Bin],
PCA-guided search for K-means,
PRL(54), No. 1, 2015, pp. 50-55.
Elsevier DOI 1502
K-means BibRef

Tsapanos, N.[Nikolaos], Tefas, A.[Anastasios], Nikolaidis, N.[Nikolaos], Pitas, I.[Ioannis],
A distributed framework for trimmed Kernel k-Means clustering,
PR(48), No. 8, 2015, pp. 2685-2698.
Elsevier DOI 1505
BibRef
And:
Kernel matrix trimming for improved Kernel K-means clustering,
ICIP15(2285-2289)
IEEE DOI 1512
Data clustering
See also Motivating class-specific nonlinear projections for single and multiple view face verification. BibRef

Soheily-Khah, S.[Saeid], Douzal-Chouakria, A.[Ahlame], Gaussier, E.[Eric],
Generalized k-means-based clustering for temporal data under weighted and kernel time warp,
PRL(75), No. 1, 2016, pp. 63-69.
Elsevier DOI 1604
Temporal data BibRef

Shantaiya, S.[Sanjivani], Verma, K.[Kesari], Mehta, K.K.[Kamal K.],
Multiple object clustering using FCM and K-means algorithms,
IJCVR(6), No. 4, 2016, pp. 331-343.
DOI Link 1610
BibRef

Rodrigues, É.O.[Érick Oliveira], Torok, L.[Leonardo], Liatsis, P.[Panos], Viterbo, J.[José], Conci, A.[Aura],
k-MS: A novel clustering algorithm based on morphological reconstruction,
PR(66), No. 1, 2017, pp. 392-403.
Elsevier DOI 1704
K-Means BibRef

Li, Z.Q.[Zhen-Qiang], Guan, X.F.[Xue-Feng], Wu, H.Y.[Hua-Yi], Gong, J.Y.[Jian-Ya],
A Novel k-Means Clustering Based Task Decomposition Method for Distributed Vector-Based CA Models,
IJGI(6), No. 4, 2017, pp. xx-yy.
DOI Link 1705
BibRef

Xu, J., Han, J., Nie, F., Li, X.,
Re-Weighted Discriminatively Embedded K-Means for Multi-View Clustering,
IP(26), No. 6, June 2017, pp. 3016-3027.
IEEE DOI 1705
Algorithm design and analysis, Clustering algorithms, Feature extraction, Iterative methods, Linear programming, Optimization, Robustness, Multi-view clustering, discriminatively embedded k-means, iterative re-weighted least squares, low-dimensional, subspace BibRef

Bai, L.[Liang], Cheng, X.Q.[Xue-Qi], Liang, J.[Jiye], Shen, H.[Huawei], Guo, Y.[Yike],
Fast density clustering strategies based on the k-means algorithm,
PR(71), No. 1, 2017, pp. 375-386.
Elsevier DOI 1707
Cluster, analysis BibRef

Zhou, X.B.[Xiang-Bing], Gu, J.G.[Jiang-Gang], Shen, S.P.[Shao-Peng], Ma, H.J.[Hong-Jiang], Miao, F.[Fang], Zhang, H.[Hua], Gong, H.M.[Hua-Ming],
An Automatic K-Means Clustering Algorithm of GPS Data Combining a Novel Niche Genetic Algorithm with Noise and Density,
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801
BibRef

Papp, D.[Dávid], Szucs, G.[Gábor],
MMKK++ algorithm for clustering heterogeneous images into an unknown number of clusters,
ELCVIA(16), No. 3, 2017, pp. 30-45.
DOI Link 1801
min-max kernel K-means plusplus. BibRef

Ismkhan, H.[Hassan],
I-k-means-+: An iterative clustering algorithm based on an enhanced version of the k-means,
PR(79), 2018, pp. 402-413.
Elsevier DOI 1804
Solution improving, Accurate k-means, Iterative improvement BibRef

Márquez, D.G.[David G.], Otero, A.[Abraham], Félix, P.[Paulo], García, C.A.[Constantino A.],
A novel and simple strategy for evolving prototype based clustering,
PR(82), 2018, pp. 16-30.
Elsevier DOI 1806
Evolving clustering, Data stream, Concept drift, Gaussian mixture models, K-means, Cluster evolution BibRef

Schellekens, V., Jacques, L.,
Quantized Compressive K-Means,
SPLetters(25), No. 8, August 2018, pp. 1211-1215.
IEEE DOI 1808
computational complexity, data compression, image coding, learning (artificial intelligence), pattern clustering, k-means clustering BibRef

Dong, L., He, L., Mao, M., Kong, G., Wu, X., Zhang, Q., Cao, X., Izquierdo, E.,
CUNet: A Compact Unsupervised Network For Image Classification,
MultMed(20), No. 8, August 2018, pp. 2012-2021.
IEEE DOI 1808
feature extraction, image classification, learning (artificial intelligence), neural nets, K-means BibRef

Gong, W.K.[Wei-Kang], Zhao, R.[Renbo], Grünewald, S.[Stefan],
Structured sparse K-means clustering via Laplacian smoothing,
PRL(112), 2018, pp. 63-69.
Elsevier DOI 1809
Structured sparse clustering, -means clustering, Feature selection, Graph Laplacian smoothing BibRef

Ganguly, D.[Debasis],
A Fast Partitional Clustering Algorithm based on Nearest Neighbours Heuristics,
PRL(112), 2018, pp. 198-204.
Elsevier DOI 1809
Tweet clustering, Scalable K-means, Inverted index BibRef

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Fast automatic estimation of the number of clusters from the minimum inter-center distance for k-means clustering,
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Mutual information, Standards, Entropy, Neural networks, Context modeling, Data models, Analytical models, Deep clustering, multilogit regression BibRef

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PAMI(44), No. 5, May 2022, pp. 2371-2385.
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Clustering algorithms, Optimization, Minimization, Heuristic algorithms, Time complexity, Sparse matrices, Lloyd heuristic BibRef

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Clustering, Hierarchical clustering, Hierarchical relationship, k-means, Cluster Number Assisted k-Means (CNAK) BibRef

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Image segmentation, Machine learning algorithms, Clustering algorithms, Machine learning, BibRef

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Wasserstein k-means with sparse simplex projection,
ICPR21(1627-1634)
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Degradation, Histograms, Heuristic algorithms, Clustering algorithms, Sparse matrices, Proposals BibRef

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Feature extraction, Manifolds, Clustering algorithms, Approximation algorithms, Eigenvalues and eigenfunctions, Graph embedded BibRef

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Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function,
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data mining, fuzzy set theory, pattern clustering, K-means algorithm, Z value, Z-test proprieties, efficiency k-means clustering, input data points, Clustering algorithms, Complexity theory, Data mining, Sociology, Z-test, clustering, data mining, k, means BibRef

Ye, Y.K.[Yong-Kai], Liu, X., Yin, J., Zhu, E.,
Co-regularized kernel k-means for multi-view clustering,
ICPR16(1583-1588)
IEEE DOI 1705
Algorithm design and analysis, Clustering algorithms, Eigenvalues and eigenfunctions, Iterative methods, Kernel, Optimization, Training BibRef