14.2.15 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.

Hartigan, J.A., Wong, M.A.,
A k-means clustering algorithm,
AppStat(28), 1979, pp. 100-108.
DOI Link BibRef 7900

Selim, S.Z., and Ismail, M.A.,
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.,
Adaptive Classifier Based on K-Means Clustering and Dynamic Programming,
OptEng(36), No. 1, 1997, pp. 31-38. Journal ref. may not be right. BibRef 9700

Navarro, A.,
A Dynamic Feature Classifier Based on Dynamic Programming and Clustering,
ICDAR97(Poste) 9708
In program, not in proceedings. BibRef

Chen, C.W., Luo, J.B., Parker, K.J.,
Image Segmentation Via Adaptive K-Mean Clustering And Knowledge-Based Morphological Operations With Biomedical Applications,
IP(7), No. 12, December 1998, pp. 1673-1683.
IEEE DOI 9812
BibRef

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
BibRef

Tyree, E.W., Long, J.A.,
A Monte Carlo Evaluation of the Moving Method, K-means and Self-Organising Neural Networks,
PAA(1), No. 2, 1998, pp. 79-90. BibRef 9800

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
BibRef

Ng, M.K.[Michael K.],
A note on constrained k-means algorithms,
PR(33), No. 3, March 2000, pp. 515-519.
WWW Link. 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.
WWW Link. BibRef 9810

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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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

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.
WWW Link. 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.
WWW Link. 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.
WWW Link. 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

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

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

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

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.[Huayi], 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.[Xueqi], 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


Ye, Y.[Yongkai], 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

Xu, J.L.[Jing-Lin], Han, J.W.[Jun-Wei], Nie, F.P.[Fei-Ping],
Discriminatively Embedded K-Means for Multi-view Clustering,
CVPR16(5356-5364)
IEEE DOI 1612
BibRef

Hassanzadeh, A.[Aidin], Kaarna, A.[Arto], Kauranne, T.[Tuomo],
Outlier Robust Geodesic K-means Algorithm for High Dimensional Data,
SSSPR16(252-262).
Springer DOI 1611
BibRef

Luchi, D.[Diego], Santos, W.[Willian], Rodrigues, A.[Alexandre], Varejão, F.M.[Flávio Miguel],
Genetic Sampling k-means for Clustering Large Data Sets,
CIARP15(691-698).
Springer DOI 1511
BibRef

Choi, Y.K.[Yu-Kyung], Park, C.[Chaehoon], Kweon, I.S.[In So],
Accelerated Kmeans Clustering Using Binary Random Projection,
ACCV14(II: 257-272).
Springer DOI 1504
BibRef

Fu, X.[Xiping], McCane, B.[Brendan], Mills, S.[Steven], Albert, M.[Michael],
NOKMeans: Non-Orthogonal K-means Hashing,
ACCV14(I: 162-177).
Springer DOI 1504
BibRef

Yu, Z.D.[Zhi-Ding], Xu, C.J.[Chun-Jing], Meng, D.Y.[De-Yu], Hui, Z.[Zhuo], Xiao, F.Y.[Fan-Yi], Liu, W.[Wenbo], Liu, J.Z.[Jian-Zhuang],
Transitive Distance Clustering with K-Means Duality,
CVPR14(987-994)
IEEE DOI 1409
BibRef

Aroche-Villarruel, A.A.[Argenis A.], Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.[José F.], Olvera-López, J.A.[J. Arturo], Pérez-Suárez, A.[Airel],
Study of Overlapping Clustering Algorithms Based on Kmeans through FBcubed Metric,
MCPR14(112-121).
Springer DOI 1407
BibRef

Nakouri, H.[Haïfa], Limam, M.[Mohamed],
Automatic Feature Detection and Clustering Using Random Indexing,
ICISP14(586-593).
Springer DOI 1406
BibRef
Earlier:
Discovering Features Contexts from Images Using Random Indexing,
IWCIA14(134-145).
Springer DOI 1405
BibRef

Li, Q.[Qun], Qin, Z.[Zhen], Chai, L.S.[Lun-Shao], Zhang, H.G.[Hong-Gang], Guo, J.[Jun], Bhanu, B.[Bir],
Representative reference-set and betweenness centrality for scene image categorization,
ICIP13(3254-3258)
IEEE DOI 1402
K-means BibRef

Norouzi, M.[Mohammad], Fleet, D.J.[David J.],
Cartesian K-Means,
CVPR13(3017-3024)
IEEE DOI 1309
approximate nearest neighbor search BibRef

He, K.[Kaiming], Wen, F.[Fang], Sun, J.[Jian],
K-Means Hashing: An Affinity-Preserving Quantization Method for Learning Binary Compact Codes,
CVPR13(2938-2945)
IEEE DOI 1309
binary embedding; hash; nearest neighbor search BibRef

Havens, T.C.[Timothy C.],
Approximation of kernel k-means for streaming data,
ICPR12(509-512).
WWW Link. 1302
BibRef

Li, Z.[Zeyu], Vinyals, O.[Oriol], Baker, H.[Harlyn], Bajcsy, R.[Ruzena],
Feature learning using Generalized Extreme Value distribution based K-means clustering,
ICPR12(1538-1541).
WWW Link. 1302
BibRef

Chavez, A.[Aaron], Gustafson, D.[David],
Building an Effective Visual Codebook: Is K-means Clustering Useful?,
ISVC12(II: 517-525).
Springer DOI 1209
BibRef

Wang, J.[Jing], Wang, J.D.[Jing-Dong], Ke, Q.[Qifa], Zeng, G.[Gang], Li, S.P.[Shi-Peng],
Fast approximate k-means via cluster closures,
CVPR12(3037-3044).
IEEE DOI 1208
BibRef

Thomas, J.C.R.[Juan Carlos Rojas],
A New Clustering Algorithm Based on K-Means Using a Line Segment as Prototype,
CIARP11(638-645).
Springer DOI 1111
BibRef

Jamil, N.[Nursuriati], Saadan, S.A.[Siti Aisyah],
Automatic Image Annotation Using Color K-Means Clustering,
IVIC09(645-652).
Springer DOI 0911
BibRef

Hung, C.C.[Chih-Cheng], Wan, L.[Li],
Hybridization of particle swarm optimization with the K-Means algorithm for image classification,
CIIP09(60-64).
IEEE DOI 0903
BibRef

Zhang, S.H.[Shao-Hong], Wong, H.S.[Hau-San],
Partial closure-based constrained clustering with order ranking,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Kashima, H.[Hisashi], Hu, J.Y.[Jian-Ying], Ray, B.[Bonnie], Singh, M.[Moninder],
K-means clustering of proportional data using L1 distance,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Asgharbeygi, N.[Nima], Maleki, A.[Arian],
Geodesic K-means clustering,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Oike, H.[Hiroshi], Wu, H.Y.[Hai-Yuan], Wada, T.[Toshikazu],
Adaptive selection of non-target cluster centers for K-means tracker,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Ullah, S.[Sameeh], Karray, F.[Fakhri], Won, J.M.[Jin-Myung],
Non-dominated Sorting Evolution Strategy-based K-means clustering algorithm for accent classification,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Liu, X.Z.[Xiao-Zhang], Feng, G.C.[Guo-Can],
Kernel Bisecting k-means clustering for SVM training sample reduction,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Bloisi, D.D.[Domenico Daniele], Iocchi, L.[Luca],
Rek-Means: A k-Means Based Clustering Algorithm,
CVS08(xx-yy).
Springer DOI 0805
BibRef

Ober, S.[Sandra], Winter, M.[Martin], Arth, C.[Clemens], Bischof, H.[Horst],
Dual-Layer Visual Vocabulary Tree Hypotheses for Object Recognition,
ICIP07(VI: 345-348).
IEEE DOI 0709
Multilevel K-Means. BibRef

Li, Z.G.[Zhen-Guo], Liu, J.Z.[Jian-Zhuang],
Constrained clustering by spectral kernel learning,
ICCV09(421-427).
IEEE DOI 0909
BibRef

Li, Z.G.[Zhen-Guo], Liu, J.Z.[Jian-Zhuang], Tang, X.[Xiaoou],
Constrained clustering via spectral regularization,
CVPR09(421-428).
IEEE DOI 0906
Pairwise constraints. Must-have and must-not-have constraints. BibRef

Ayaquica-Martínez, I.O., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.[J. Ariel],
Conceptual K-Means Algorithm Based on Complex Features,
CIARP06(491-501).
Springer DOI 0611
BibRef

Bouguessa, M.[Mohamed], Wang, S.R.[Sheng-Rui], Jiang, Q.S.[Qing-Shan],
A K-means-based Algorithm for Projective Clustering,
ICPR06(I: 888-891).
IEEE DOI 0609
BibRef

Cheng, S.S.[Shih-Sian], Chao, Y.H.[Yi-Hsiang], Wang, H.M.[Hsin-Min], Fu, H.C.[Hsin-Chia],
A Prototypes-Embedded Genetic K-means Algorithm,
ICPR06(II: 724-727).
IEEE DOI 0609
BibRef

Morii, F.[Fujiki],
A Generalized K-Means Algorithm with Semi-Supervised Weight Coefficients,
ICPR06(III: 198-201).
IEEE DOI 0609
BibRef

Qiu, B.[Bo], Xu, C.S.[Chang Sheng], Tian, Q.[Qi],
Efficient Relevance Feedback Using Semi-supervised Kernel-specified K-means Clustering,
ICPR06(III: 316-319).
IEEE DOI 0609
BibRef

Hautamäki, V.[Ville], Cherednichenko, S.[Svetlana], Kärkkäinen, I.[Ismo], Kinnunen, T.[Tomi], Fränti, P.[Pasi],
Improving K-Means by Outlier Removal,
SCIA05(978-987).
Springer DOI 0506
BibRef

Xu, M.[Mantao], Franti, P.,
A heuristic k-means clustering algorithm by kernel pca,
ICIP04(V: 3503-3506).
IEEE DOI 0505
BibRef

Xu, M.[Mantao], Franti, P.,
Delta-MSE dissimilarity in suboptimal K-means clustering,
ICPR04(IV: 577-580).
IEEE DOI 0409
BibRef

Zhang, R.[Rong], Rudnicky, A.I.,
A large scale clustering scheme for kernel k-means,
ICPR02(IV: 289-292).
IEEE DOI 0211
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
ISODATA Clustering .


Last update:Sep 25, 2017 at 16:36:46