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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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

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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

Karmitsa, N.[Napsu], Eronen, V.P.[Ville-Pekka], Mäkelä, M.M.[Marko M.], Pahikkala, T.[Tapio], Airola, A.[Antti],
Stochastic limited memory bundle algorithm for clustering in big data,
PR(165), 2025, pp. 111654.
Elsevier DOI 2505
Clustering, Nonsmooth optimization, Nonconvex optimization, Stochastic gradient, Limited memory bundle method 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

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A Fast Partitional Clustering Algorithm based on Nearest Neighbours Heuristics,
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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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Elsevier DOI 1812
Data clustering, Inter-center distance, Center-based clustering, Cluster number estimation BibRef

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How much can k-means be improved by using better initialization and repeats?,
PR(93), 2019, pp. 95-112.
Elsevier DOI 1906
Clustering algorithms, K-means, Initialization, Clustering accuracy, Prototype selection BibRef

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k-means as a variational EM approximation of Gaussian mixture models,
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Elsevier DOI 1909
k-means, Gaussian mixture models, Expectation maximization, Variational methods, Free energy BibRef

Yu, H.[Hao], Wen, G.Q.[Guo-Qiu], Gan, J.Z.[Jiang-Zhang], Zheng, W.[Wei], Lei, C.[Cong],
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Moradi Fard, M.[Maziar], Thonet, T.[Thibaut], Gaussier, E.[Eric],
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Deep clustering, k-Means, Deep learning, Clustering BibRef

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CNAK: Cluster number assisted K-means,
PR(110), 2021, pp. 107625.
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clustering, Bipartite graph, Perfect matching, algorithm, Stability BibRef

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A semi-supervised sparse K-Means algorithm,
PRL(142), 2021, pp. 65-71.
Elsevier DOI 2101
Semi-supervised clustering, sparse clustering, feature selection BibRef

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Memetic differential evolution methods for clustering problems,
PR(114), 2021, pp. 107849.
Elsevier DOI 2103
Global optimization, Clustering, Minimum sum-of-squares, Hybrid genetic algorithm, K-MEANS BibRef

Jabi, M.[Mohammed], Pedersoli, M.[Marco], Mitiche, A.[Amar], Ben Ayed, I.[Ismail],
Deep Clustering: On the Link Between Discriminative Models and K-Means,
PAMI(43), No. 6, June 2021, pp. 1887-1896.
IEEE DOI 2106
Mutual information, Standards, Entropy, Neural networks, Context modeling, Data models, Analytical models, Deep clustering, multilogit regression BibRef

Huang, S.D.[Shu-Dong], Kang, Z.[Zhao], Xu, Z.L.[Zeng-Lin], Liu, Q.H.[Quan-Hui],
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PR(117), 2021, pp. 107996.
Elsevier DOI 2106
-means algorithm, Robust clustering, Deep learning BibRef

Xia, S.Y.[Shu-Yin], Peng, D.[Daowan], Meng, D.Y.[De-Yu], Zhang, C.Q.[Chang-Qing], Wang, G.Y.[Guo-Yin], Giem, E.[Elisabeth], Wei, W.[Wei], Chen, Z.Z.[Zi-Zhong],
Ball k-Means: Fast Adaptive Clustering With No Bounds,
PAMI(44), No. 1, January 2022, pp. 87-99.
IEEE DOI 2112
Clustering algorithms, Approximation algorithms, Acceleration, Partitioning algorithms, Standards, Laboratories, Time complexity, neighbor cluster BibRef

Nie, F.P.[Fei-Ping], Xue, J.J.[Jing-Jing], Wu, D.Y.[Dan-Yang], Wang, R.[Rong], Li, H.[Hui], Li, X.L.[Xue-Long],
Coordinate Descent Method for k-means,
PAMI(44), No. 5, May 2022, pp. 2371-2385.
IEEE DOI 2204
Clustering algorithms, Optimization, Minimization, Heuristic algorithms, Time complexity, Sparse matrices, Lloyd heuristic BibRef

Wang, R.[Rong], Lu, J.[Jitao], Lu, Y.H.[Yi-Hang], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
Discrete and Parameter-Free Multiple Kernel k-Means,
IP(31), No. 2022, pp. 2796-2808.
IEEE DOI 2204
Kernel, Clustering algorithms, Optimization, Correlation, Analytical models, Redundancy, Matrices, Kernel method, coordinate descent BibRef

Dorabiala, O.[Olga], Kutz, J.N.[J. Nathan], Aravkin, A.Y.[Aleksandr Y.],
Robust Trimmed K-Means,
PRL(161), 2022, pp. 9-16.
Elsevier DOI 2209
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Lin, Y.X.[Yun-Xia], Chen, S.C.[Song-Can],
Rectified Euler k-means and beyond,
PR(137), 2023, pp. 109283.
Elsevier DOI 2302
Kernel -means, Euler kernel, Pseudo centroid, Rectified euler -means BibRef

Mussabayev, R.[Rustam], Mladenovic, N.[Nenad], Jarboui, B.[Bassem], Mussabayev, R.[Ravil],
How to Use K-means for Big Data Clustering?,
PR(137), 2023, pp. 109269.
Elsevier DOI 2302
Big data, Clustering, Minimum sum-of-squares, Divide and conquer algorithm, Decomposition, K-means, Unsupervised learning BibRef

Laber, E.[Eduardo], Murtinho, L.[Lucas], Oliveira, F.[Felipe],
Shallow decision trees for explainable k-means clustering,
PR(137), 2023, pp. 109239.
Elsevier DOI 2302
Clustering, Explainability, K-means, Decision trees BibRef

Liu, X.W.[Xin-Wang],
SimpleMKKM: Simple Multiple Kernel K-Means,
PAMI(45), No. 4, April 2023, pp. 5174-5186.
IEEE DOI 2303
Kernel, Optimization, Clustering algorithms, Minimization, Partitioning algorithms, Linear programming, Task analysis, kernel alignment maximization BibRef

Hu, H.[Haize], Liu, J.X.[Jian-Xun], Zhang, X.P.[Xiang-Ping], Fang, M.G.[Meng-Ge],
An Effective and Adaptable K-means Algorithm for Big Data Cluster Analysis,
PR(139), 2023, pp. 109404.
Elsevier DOI 2304
-means algorithm, Local optimization, Lévy flight, Global search, Clustering centroids BibRef

Rezaei, M.[Mohammad], Fränti, P.[Pasi],
K-sets and k-swaps algorithms for clustering sets,
PR(139), 2023, pp. 109454.
Elsevier DOI 2304
Wrapper to prevent local minima. Clustering sets, Similarity of sets, -means, -medoids, Random swap, K-swaps, Customer segmentation, Clustering healthcare records BibRef

Liu, H.F.[Hong-Fu], Chen, J.X.[Jun-Xiang], Dy, J.[Jennifer], Fu, Y.[Yun],
Transforming Complex Problems Into K-Means Solutions,
PAMI(45), No. 7, July 2023, pp. 9149-9168.
IEEE DOI 2306
Clustering algorithms, Linear programming, Standards, Iterative methods, Anomaly detection, Euclidean distance, outlier detection BibRef

Xin, H.[Haonan], Lu, Y.H.[Yi-Hang], Tang, H.L.[Hao-Liang], Wang, R.[Rong], Nie, F.P.[Fei-Ping],
Self-Weighted Euler k-Means Clustering,
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IEEE DOI 2310
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Guan, X.[Xin], Terada, Y.[Yoshikazu],
Sparse kernel k-means for high-dimensional data,
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Elsevier DOI 2310
Clustering, Feature selection, Kernel method BibRef

Ping, Y.[Yuan], Li, H.[Huina], Hao, B.[Bin], Guo, C.[Chun], Wang, B.[Baocang],
Beyond k-Means++: Towards better cluster exploration with geometrical information,
PR(146), 2024, pp. 110036.
Elsevier DOI 2311
Cluster analysis, k-means++, Support vector data description, Edge pattern, Division and aggregation BibRef

He, L.[Li], Zhang, H.[Hong],
Doubly Stochastic Distance Clustering,
CirSysVideo(33), No. 11, November 2023, pp. 6721-6732.
IEEE DOI 2311
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Lu, H.[Han], Xu, H.[Huafu], Wang, Q.Q.[Qian-Qian], Gao, Q.X.[Quan-Xue], Yang, M.[Ming], Gao, X.B.[Xin-Bo],
Efficient Multi-View K-Means for Image Clustering,
IP(33), 2024, pp. 273-284.
IEEE DOI 2401
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Han, S.[Soohee], Lee, J.[Jeongho],
Parallelized Inter-Image k-Means Clustering Algorithm for Unsupervised Classification of Series of Satellite Images,
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Shi, K.[Kegong], Yan, J.J.[Jin-Jin], Yang, J.[Jinquan],
A Semantic Partition Algorithm Based on Improved K-Means Clustering for Large-Scale Indoor Areas,
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Pal, S.S.[Shankho Subhra], Mukhopadhyay, J.[Jayanta], Sarkar, S.[Sudeshna],
Finding hierarchy of clusters,
PRL(178), 2024, pp. 7-13.
Elsevier DOI 2402
Clustering, Hierarchical clustering, Hierarchical relationship, k-means, Cluster Number Assisted k-Means (CNAK) BibRef

Su, R.[Rina], Guo, Y.[Yu], Wu, C.[Caiying], Jin, Q.Y.[Qi-Yu], Zeng, T.Y.[Tie-Yong],
Kernel correlation-dissimilarity for Multiple Kernel k-Means clustering,
PR(150), 2024, pp. 110307.
Elsevier DOI 2403
k-means, Multiple kernel learning, Consistency, Frobenius inner product, Manhattan distance BibRef

Wu, X.L.[Xiao-Ling], Yu, Y.F.[Yu-Feng], Chen, L.[Long], Ding, W.P.[Wei-Ping], Wang, Y.X.[Ying-Xu],
Robust deep fuzzy K-means clustering for image data,
PR(153), 2024, pp. 110504.
Elsevier DOI 2405
Locality preserving, Deep convolutional autoencoder, Laplacian regularization, Unsupervised image clustering BibRef

Heidari, J., Daneshpour, N., Zangeneh, A.,
A novel K-means and K-medoids algorithms for clustering non-spherical-shape clusters non-sensitive to outliers,
PR(155), 2024, pp. 110639.
Elsevier DOI 2408
Initial centers, Number of clusters, Overlap space, Non-spherical BibRef

Zhang, X.D.[Xiang-Dong], Li, F.F.[Fang-Fang], Shi, Z.Y.[Zhao-Yang], Yang, M.[Ming],
Multi-view reduced dimensionality K-means clustering with sigma-norm and Schatten p-norm,
PR(155), 2024, pp. 110675.
Elsevier DOI 2408
Multi-view, Dimensionality reduction, Matrix sigma-norm, Schatten p-norm BibRef

Huang, X.[Xiuqi], Tao, H.[Hong], Ni, H.T.[Hao-Tian], Hou, C.P.[Chen-Ping],
Debiasing weighted multi-view k-means clustering based on causal regularization,
PR(160), 2025, pp. 111195.
Elsevier DOI 2501
Multi-view, Clustering, Covariate balance, Causal regularization BibRef

Zhang, Z.T.[Zi-Tong], Chen, X.J.[Xiao-Jun], Wang, C.[Chen], Wang, R.[Ruili], Song, W.[Wei], Nie, F.P.[Fei-Ping],
Structured multi-view k-means clustering,
PR(160), 2025, pp. 111113.
Elsevier DOI 2501
Clustering, Multi-view clustering, -means, Structure learning BibRef

Pei, S.F.[Shen-Fei], Sun, Y.C.[Yuan-Chen], Nie, F.P.[Fei-Ping], Jiang, X.D.[Xu-Dong], Zheng, Z.W.[Zeng-Wei],
Adaptive Graph K-Means,
PR(161), 2025, pp. 111226.
Elsevier DOI 2502
Machine learning, Clustering, Graph-based, -means, Computational efficiency BibRef

Huang, C.Y.[Cheng-Ying], Wu, Z.[Zhengda], Xi, H.[Heran], Zhu, J.H.[Jing-Hua],
kMaXU: Medical image segmentation U-Net with k-means Mask Transformer and contrastive cluster assignment,
PR(161), 2025, pp. 111274.
Elsevier DOI Code:
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U-shaped network, Convolutional neural network, Mask Transformer, Medical image segmentation, Cluster assignments BibRef

Gao, Q.X.[Quan-Xue], Li, F.F.[Fang-Fang], Wang, Q.Q.[Qian-Qian], Gao, X.B.[Xin-Bo], Tao, D.C.[Da-Cheng],
Manifold Based Multi-View K-Means,
PAMI(47), No. 4, April 2025, pp. 3175-3182.
IEEE DOI 2503
Manifolds, Kernel, Tensors, Manifold learning, Estimation, Nearest neighbor methods, Buildings, Accuracy, Vectors, tensor schatten p-norm BibRef

Yang, M.S.[Miin-Shen], Sinaga, K.P.[Kristina P.],
Federated Multi-View K-Means Clustering,
PAMI(47), No. 4, April 2025, pp. 2446-2459.
IEEE DOI 2503
Clustering algorithms, Federated learning, Distributed databases, Data models, Data privacy, Machine learning algorithms, Kernel, privacy BibRef

Meng, B.[Bin], Li, F.F.[Fang-Fang], Yang, F.[Fan], Gao, Q.X.[Quan-Xue],
Centroid-Free K-Means With Balanced Clustering,
SPLetters(32), 2025, pp. 1191-1195.
IEEE DOI 2503
Clustering algorithms, Manifolds, Manifold learning, Signal processing algorithms, Optimization, K-means BibRef

Bajpai, N.[Namita], Paik, J.H.[Jiaul H.], Sarkar, S.[Sudeshna],
Balanced seed selection for K-means clustering with determinantal point process,
PR(164), 2025, pp. 111548.
Elsevier DOI 2504
K-means, Determinantal point process BibRef

Fu, Z.Q.[Zhi-Qiang], Zhao, Y.[Yao], Chang, D.X.[Dong-Xia], Wang, Y.M.[Yi-Ming], Wen, J.[Jie],
Reordered k-Means: A New Baseline for View-Unaligned Multi-View Clustering,
MultMed(27), 2025, pp. 1962-1972.
IEEE DOI 2504
Clustering methods, Spatiotemporal phenomena, Information science, Matrix converters, Image color analysis, matrix factorization BibRef

Garcin, C.[Camille], Servajean, M.[Maximilien], Joly, A.[Alexis], Salmon, J.[Joseph],
A Two-Head Loss Function for Deep Average-K Classification,
WACV25(7358-7367)
IEEE DOI 2505
Average-K as opposed to top-K. Measurement, Deep learning, Head, Accuracy, Uncertainty, Memory management, Multi label classification, Tail, deep learning BibRef

Zaech, J.N.[Jan-Nico], Danelljan, M.[Martin], Birdal, T.[Tolga], Van Gool, L.J.[Luc J.],
Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing,
CVPR24(26191-26201)
IEEE DOI 2410
Visualization, Quantum computing, Costs, Current measurement, Posterior probability, Prototypes, Quantum Computing, Clustering, uncertainty estimation BibRef

Miao, S.Y.[Shu-Yu], Zheng, L.[Lin], Liu, J.J.[Jing-Jing], Jin, H.[Hong],
K-means Clustering Based Feature Consistency Alignment for Label-free Model Evaluation,
VDU23(3299-3307)
IEEE DOI 2309
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Lu, Y.H.[Yi-Hang], Zheng, X.[Xuan], Wang, R.[Rong], Nie, F.P.[Fei-Ping], Li, X.L.[Xue-Long],
A Unified Framework for Discrete Multi-kernel k-means with Kernel Diversity Regularization,
ICPR22(4934-4940)
IEEE DOI 2212
Correlation, Diversity reception, Redundancy, Boosting, Kernel, Task analysis BibRef

Goel, A.[Anurag], Majumdar, A.[Angshul], Chouzenoux, E.[Emilie], Chierchia, G.[Giovanni],
Deep Convolutional K-Means Clustering,
ICIP22(211-215)
IEEE DOI 2211
Deep learning, Training, Representation learning, Transforms, Benchmark testing, Decoding, Convolutional Neural Network, Convolutional Transform Learning BibRef

Qian, Q.[Qi], Xu, Y.H.[Yuan-Hong], Hu, J.[Juhua], Li, H.[Hao], Jin, R.[Rong],
Unsupervised Visual Representation Learning by Online Constrained K-Means,
CVPR22(16619-16628)
IEEE DOI 2210
Representation learning, Training, Visualization, Transformers, Data structures, Computational efficiency, Self- semi- meta- unsupervised learning BibRef

Ren, Y.H.[Yuan-Hang], Du, Y.[Ye],
Uniform and Non-uniform Sampling Methods for Sub-linear Time k-means Clustering,
ICPR21(7775-7781)
IEEE DOI 2105
Image segmentation, Machine learning algorithms, Clustering algorithms, Machine learning, BibRef

Fukunaga, T.[Takumi], Kasai, H.[Hiroyuki],
Wasserstein k-means with sparse simplex projection,
ICPR21(1627-1634)
IEEE DOI 2105
Degradation, Histograms, Heuristic algorithms, Clustering algorithms, Sparse matrices, Proposals BibRef

Chen, Q., Jiang, J., Du, M., Zhou, L., Jing, C., Lu, C.,
A Hybridization of An Improved Particle Swarm Optimization and Fuzzy K-means Algorithm for Hyperspectral Image Classification,
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Le, H.M.[Huu M.], Eriksson, A.P.[Anders P.], Do, T.T.[Thanh-Toan], Milford, M.[Michael],
A Binary Optimization Approach for Constrained K-Means Clustering,
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Springer DOI 1906
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Cai, G., Zhang, R., Nie, F., Li, X.,
Feature Selection via Incorporating Stiefel Manifold in Relaxed K-Means,
ICIP18(1503-1507)
IEEE DOI 1809
Feature extraction, Manifolds, Clustering algorithms, Approximation algorithms, Eigenvalues and eigenfunctions, Graph embedded BibRef

Rastogi, R.[Reshma], Sharma, S.[Sweta],
Tree-Based Structural Twin Support Tensor Clustering with Square Loss Function,
PReMI17(28-34).
Springer DOI 1711
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Kumar, R.[Ritesh], Bishnu, P.S.[Partha Sarathi], Bhattacherjee, V.[Vandana],
K-Means Algorithm to Identify k1-Most Demanding Products,
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Springer DOI 1711
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Fatima, E.B., Abdelmajid, E.M.,
Study of efficiency k-means clustering using Z-test proprieties,
ISCV17(1-5)
IEEE DOI 1710
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