14.2.6 Iterative, Hierarchical Clustering Techniques

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PR(3), No. 1, April 1971, pp. 23-30.
Elsevier DOI 0309
A two layered pattern recognition program. The first layer scans an input for features and produces a coded representation. The second layer looks for combinations of code which signify relations between features in the input. BibRef

Haralick, R.M.[Robert M.], Dinstein, I.,
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Biehl, L.L., and Silva, L.F.,
A Multilevel Multispectral Data Set Analysis in the Visible and Infrared Wavelength Regions,
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Hierarchical agglomerative clustering procedure,
PR(11), No. 5-6, 1979, pp. 365-381.
Elsevier DOI 0309
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Smith, S.P.[Stephen P.], Dubes, R.C.[Richard C.],
Stability of a hierarchical clustering,
PR(12), No. 3, 1980, pp. 177-187.
Elsevier DOI 0309
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Ozawa, K.[Kazumasa],
Classic: A hierarchical clustering algorithm based on asymmetric similarities,
PR(16), No. 2, 1983, pp. 201-211.
Elsevier DOI 0309
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Ozawa, K.[Kazumasa],
A stratificational overlapping cluster scheme,
PR(18), No. 3-4, 1985, pp. 279-286.
Elsevier DOI 0309
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Perruchet, C.[Christophe],
Constrained agglomerative hierarchical classification,
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Elsevier DOI 0309
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Schuermann, J.[Juergen], Doster, W.[Wolfgang],
A decision theoretic approach to hierarchical classifier design,
PR(17), No. 3, 1984, pp. 359-369.
Elsevier DOI 0309
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López de Màntaras, R., Aguilar-Martín, J.,
Self-learning pattern classification using a sequential clustering technique,
PR(18), No. 3-4, 1985, pp. 271-277.
Elsevier DOI 0309
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Jain, N.C.[Naresh C.], Indrayan, A.[Abhaya], Goel, L.R.[Lajpat R.],
Monte Carlo comparison of six hierarchical clustering methods on random data,
PR(19), No. 1, 1986, pp. 95-99.
Elsevier DOI 0309
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Plastria, F.[Frank],
Two hierarchies associated with each clustering scheme,
PR(19), No. 2, 1986, pp. 193-196.
Elsevier DOI 0309
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Kurzynski, M.W.[Marek W.],
On the Identity of Optimal Strategies for Multistage Classifiers,
PRL(10), No. 1, 1989, pp. 39-46.
See also optimal strategy of a tree classifier, The.
See also On the multistage Bayes classifier. BibRef 8900

Kurzynski, M.W., Puchala, E., Blinowska, A.,
A branch-and-bound algorithm for optimization of multiperspective classifier,
ICPR94(B:235-239).
IEEE DOI 9410
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Kurzynski, M.W., Puchala, E.,
Algorithms of the multiperspective pattern recognition,
ICPR92(II:627-630).
IEEE DOI 9208
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Spivak, S.[Shalom],
A multisurface method for pattern classification,
PR(22), No. 5, 1989, pp. 587-591.
Elsevier DOI 0309
The desired classifier is built in two stages. In the first stage an initial classifier is built. In the second stage the classifier is modified to obtain the desired properties. Modification is performed by use of the gradient descent procedure. BibRef

Spivak, S.[Shalom],
Some properties of the multisurface method for pattern classification,
PR(24), No. 4, 1991, pp. 325-330.
Elsevier DOI 0401
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Li, X.,
Parallel algorithms for hierarchical clustering and cluster validity,
PAMI(12), No. 11, November 1990, pp. 1088-1092.
IEEE DOI 0401
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Murtagh, F.,
Comments on 'Parallel algorithms for hierarchical clustering and cluster validity',
PAMI(14), No. 10, October 1992, pp. 1056-1057.
IEEE DOI 0401
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Zhang, Q.W.[Qi-Wen], Boyle, R.D.[Roger D.],
A new clustering algorithm with multiple runs of iterative procedures,
PR(24), No. 9, 1991, pp. 835-848.
Elsevier DOI 0401
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Chan, K.P., Cheung, Y.S.,
Clustering of clusters,
PR(25), No. 2, February 1992, pp. 211-217.
Elsevier DOI 0401
Character recognition. BibRef

Liaw, J.N.[Jin-Nan], Kashyap, R.L.,
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PR(27), No. 10, October 1994, pp. 1423-1438.
Elsevier DOI 0401
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Zavaljevski, A., Dhawan, A.P., Kelch, D.J., Riddell, J.,
Adaptive Multilevel Classification and Detection in Multispectral Images,
OptEng(35), No. 10, October 1996, pp. 2884-2893. 9611
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El-Sonbaty, Y.[Yasser], Ismail, M.A.,
On-line hierarchical clustering,
PRL(19), No. 14, December 1998, pp. 1285-1291. BibRef 9812

Fisher, D.,
Iterative Optimization and Simplification of Hierarchical Clusterings,
JAIR(4), 1996, pp. 147-178.
HTML Version. BibRef 9600

Labonté, G.,
On a Neural Network that Performs an Enhanced Nearest-Neighbour Matching,
PAA(3), No. 3 2000, pp. 267-278. 0010
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Leung, Y.[Yee], Zhang, J.S.[Jiang-She], Xu, Z.B.[Zong-Ben],
Clustering by Scale-Space Filtering,
PAMI(22), No. 12, December 2000, pp. 1396-1410.
IEEE DOI 0012
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Leung, Y.[Yee], Ma, J.H.[Jiang-Hong], Zhang, W.X.[Wen-Xiu],
A New Method for Mining Regression Classes in Large Data Sets,
PAMI(23), No. 1, January 2001, pp. 5-21.
IEEE DOI 0101
Genetic algorithm. Regression class: subset of data subject to regression model. BibRef

Talavera, L.[Luis], Béjar, J.[Javier],
Generality-Based Conceptual Clustering with Probabilistic Concepts,
PAMI(23), No. 2, February 2001, pp. 196-206.
IEEE DOI 0102
BibRef

Fieguth, P.W.[Paul W.],
Multiply-rooted multiscale models for large-scale estimation,
IP(10), No. 11, November 2001, pp. 1676-1686.
IEEE DOI 0201
BibRef
Earlier:
Foveated Multiscale Models for Large-Scale Estimation,
ICIP99(II:871-874).
IEEE DOI BibRef
Earlier:
Multipole-motivated reduced-state estimation,
ICIP98(I: 635-638).
IEEE DOI 9810
BibRef

Agnelli, D.[Davide], Bollini, A.[Alessandro], Lombardi, L.[Luca],
Image classification: an evolutionary approach,
PRL(23), No. 1-3, January 2002, pp. 303-309.
Elsevier DOI 0201
BibRef

Sun, Y.[Ying], Zhu, Q.M.[Qiu-Ming], Chen, Z.X.[Zheng-Xin],
An iterative initial-points refinement algorithm for categorical data clustering,
PRL(23), No. 7, May 2002, pp. 875-884.
Elsevier DOI 0203
BibRef

Lam, W.[Wai], Keung, C.K.[Chi-Kin], Liu, D.Y.[Dan-Yu],
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction,
PAMI(24), No. 8, August 2002, pp. 1075-1090.
IEEE Abstract. 0208
ICPL. Integrated Concept Prototype Learner. Integrate instance filtering with instace abstraction. BibRef

Fernández Prieto, D.,
An iterative approach to partially supervised classification problems,
JRS(23), No. 18, September 2002, pp. 3887-3892.
WWW Link. 0211

See also adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images, An. BibRef

Rodríguez, C., Soraluze, I., Muguerza, J., Martín, J.I., Álvarez, G.,
Hierarchical classifiers based on neighbourhood criteria with adaptive computational cost,
PR(35), No. 12, December 2002, pp. 2761-2769.
Elsevier DOI 0209
BibRef

Vijaya, P.A., Murty, M.N.[M. Narasimha], Subramanian, D.K.,
Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets,
PRL(25), No. 4, March 2004, pp. 505-513.
Elsevier DOI 0402
BibRef

Huber, R.[Reinhold], Ramoser, H.[Herbert], Mayer, K.[Konrad], Penz, H.[Harald], Rubik, M.[Michael],
Classification of coins using an eigenspace approach,
PRL(26), No. 1, 1 January 2005, pp. 61-75.
Elsevier DOI 0501
Multistage classifier for large classes of coins. BibRef

Hill, E.J.[E. June], Alder, M.D.[Michael D.], de Silva, C.J.S.[Christopher J.S.],
An improvement to the DR clustering algorithm,
PRL(26), No. 1, 1 January 2005, pp. 101-107.
Elsevier DOI 0501
Map data from a region to toroidal surface then run DR (Dog-Rabbit) clustering. BibRef

Lee, S., Crawford, M.M.,
Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure,
IP(14), No. 3, March 2005, pp. 312-320.
IEEE DOI 0501
BibRef

Milgram, J.[Jonathan], Sabourin, R.[Robert], Cheriet, M.[Mohamed],
Combining Model-based and Discriminative Approaches in a Modular Two-stage Classification System: Application to Isolated Handwritten Digit Recognition,
ELCVIA(5), No. 2, 2005, pp. 1-15.
DOI Link 0505
BibRef
Earlier:
Two-stage classification system combining model-based and discriminative approaches,
ICPR04(I: 152-155).
IEEE DOI 0409
BibRef

Lee, J.W.T.[John W.T.], Yeung, D.S.[Daniel S.], Tsang, E.C.C.[Eric C.C.],
Hierarchical clustering based on ordinal consistency,
PR(38), No. 11, November 2005, pp. 1913-1925.
Elsevier DOI 0509
BibRef

Amador, J.J.[José J.],
Sequential clustering by statistical methodology,
PRL(26), No. 14, 15 October 2005, pp. 2152-2163.
Elsevier DOI 0510
BibRef

Dutta, M., Mahanta, A.K.[A. Kakoti], Pujari, A.K.[Arun K.],
QROCK: A quick version of the ROCK algorithm for clustering of categorical data,
PRL(26), No. 15, November 2005, pp. 2364-2373.
Elsevier DOI 0510
An agglomerative hierarchical clustering algorithm for clustering categorical data. BibRef

Zilong, G.[Guo], Sun'an, W.[Wang], Jian, Z.[Zhuang],
A novel immune evolutionary algorithm incorporating chaos optimization,
PRL(27), No. 1, 1 January 2006, pp. 2-8.
Elsevier DOI 0512
BibRef

Nock, R.[Richard], and Nielsen, F.[Frank],
On Weighting Clustering,
PAMI(28), No. 8, August 2006, pp. 1223-1235.
IEEE DOI 0606
BibRef
Earlier:
Improving clustering algorithms through constrained convex optimization,
ICPR04(IV: 557-560).
IEEE DOI 0409
Formalize unsupervised clustering ideas to take advantage of boosting ideas. BibRef

Nock, R.[Richard], Nielsen, F.[Frank],
Bregman Divergences and Surrogates for Learning,
PAMI(31), No. 11, November 2009, pp. 2048-2059.
IEEE DOI 0910
BibRef
Earlier: A2, A1:
Bregman sided and symmetrized centroids,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning,
SPLetters(21), No. 10, October 2014, pp. 1289-1292.
IEEE DOI 1407
BibRef
Earlier:
Clustering Multivariate Normal Distributions,
ETVC08(164-174).
Springer DOI 0811
Dynamic programming BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity,
SPLetters(24), No. 8, August 2017, pp. 1123-1127.
IEEE DOI 1708
matrix algebra, Bregman divergence, comparative convexity, generalizing skew Jensen divergence, monotone embeddings, ordinary convexity, quasiarithmetic means, Convex functions, Generators, Harmonic analysis, Indexes, Q measurement, Radio frequency, Taylor series, Bregman divergence (BD), conformal divergence, convexity, quasi-arithmetic weighted mean, regular mean, skew, Jensen, divergence, (JD) BibRef

Nock, R.[Richard], Nielsen, F.[Frank],
On the efficient minimization of convex surrogates in supervised learning,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
Intrinsic Geometries in Learning,
ETVC08(175-215).
Springer DOI 0811
BibRef

Nielsen, F.[Frank], Nock, R.[Richard],
On the chi square and higher-order chi distances for approximating f-divergences,
SPLetters(21), No. 1, January 2014, pp. 10-13.
IEEE DOI 1402
Gaussian processes BibRef

Nock, R.[Richard], Vaillant, P.[Pascal], Henry, C.[Claudia], Nielsen, F.[Frank],
Soft memberships for spectral clustering, with application to permeable language distinction,
PR(42), No. 1, January 2009, pp. 43-53.
Elsevier DOI 0809
Spectral clustering; Soft membership; Stochastic processes; Text classification BibRef

Kushnir, D.[Dan], Galun, M.[Meirav], Brandt, A.[Achi],
Fast multiscale clustering and manifold identification,
PR(39), No. 10, October 2006, pp. 1876-1891.
Elsevier DOI 0606
Algebraic multigrid (AMG); Aggregation; Graph partitioning; Similarity-based clustering; Manifold; Data analysis; Astrophysical models BibRef

Kushnir, D.[Dan], Galun, M.[Meirav], Brandt, A.[Achi],
Efficient Multilevel Eigensolvers with Applications to Data Analysis Tasks,
PAMI(32), No. 8, August 2010, pp. 1377-1391.
IEEE DOI 1007
Solving systems of equations. Solve eigen value problems. BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Astigarraga, A.,
Classifier hierarchy learning by means of genetic algorithms,
PRL(27), No. 16, December 2006, pp. 1998-2004.
Elsevier DOI 0611
Data mining; Classifier combination; Genetic algorithms BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Jauregi, E., Yurramendi, Y.,
Analyzing Classifier Hierarchy Multiclassifier Learning,
CIARP08(775-782).
Springer DOI 0809
BibRef

Cao, F.[Frédéric], Delon, J.[Julie], Desolneux, A.[Agnès], Musé, P.[Pablo], Sur, F.[Frédéric],
A Unified Framework for Detecting Groups and Application to Shape Recognition,
JMIV(27), No. 2, February 2007, pp. 91-119.
Springer DOI 0704

See also grouping principle and four applications, A. Evaluate validity of clusters, containment of clusters, merging of clusters. BibRef

Santos, J.M.[Jorge M.], de Sa, J.M.[Joaquim Marques], Alexandre, L.A.[Luis A.],
LEGClust: A Clustering Algorithm Based on Layered Entropic Subgraphs,
PAMI(30), No. 1, January 2008, pp. 62-75.
IEEE DOI 0711
Builds layers of subgraphs then applies clustering. BibRef

Goldberger, J.[Jacob], Tassa, T.[Tamir],
A hierarchical clustering algorithm based on the Hungarian method,
PRL(29), No. 11, 1 August 2008, pp. 1632-1638.
Elsevier DOI 0804
Grouping; Pairwise clustering; Hierarchical clustering; Graph algorithms BibRef

Dang, E.K.F.[Edward K. F.], Luk, R.W.P.[Robert W. P.], Lee, D.L.[Dik Lun], Ho, K.S.[Kei-Shiu], Chan, S.C.F.[Stephen C. F.],
Optimal Combination of Nested Clusters by a Greedy Approximation Algorithm,
PAMI(31), No. 11, November 2009, pp. 2083-2087.
IEEE DOI 0910
BibRef

Li, X.T.[Xu-Tao], Ye, Y.M.[Yun-Ming], Li, M.J.J.[Mark Jun-Jie], Ng, M.K.[Michael K.],
On cluster tree for nested and multi-density data clustering,
PR(43), No. 9, September 2010, pp. 3130-3143.
Elsevier DOI 1006
Hierarchical clustering; Multi-densities; Cluster tree; k-Means-type algorithm BibRef

Tang, X.Q.[Xu-Qing], Zhu, P.[Ping], Cheng, J.X.[Jia-Xing],
The structural clustering and analysis of metric based on granular space,
PR(43), No. 11, November 2010, pp. 3768-3786.
Elsevier DOI 1008
Granular computing; Granular space; Normalized metric space; Clustering structural analysis; Consistent cluster; Optimal cluster; Clustering fusion BibRef

Perina, A.[Alessandro], Cristani, M.[Marco], Castellani, U.[Umberto], Murino, V.[Vittorio], Jojic, N.[Nebojsa],
Free Energy Score Spaces: Using Generative Information in Discriminative Classifiers,
PAMI(34), No. 7, July 2012, pp. 1249-1262.
IEEE DOI 1205
Hybrid generative/discriminative paradigm, variational free energy, classification. Fixed dimension feature vector for each data sample of varying size. BibRef

Bicego, M., Perina, A., Murino, V., Martins, A.F.T.[André F. T.], Aguiar, P.M.Q.[Pedro M. Q.], Figueiredo, M.A.T.,
Combining free energy score spaces with information theoretic kernels: Application to scene classification,
ICIP10(2661-2664).
IEEE DOI 1009
Classifiers for structured objects. BibRef

Li, C.Z.[Chun-Zhong], Xu, Z.B.[Zong-Ben], Luo, T.[Tao],
A heuristic hierarchical clustering based on multiple similarity measurements,
PRL(34), No. 2, 15 January 2013, pp. 155-162.
Elsevier DOI 1212
Data mining; Agglomerative clustering; Heuristic; Blurring; Top-down; Structural nearest neighbor BibRef

Pérez-Suárez, A.[Airel], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesús A.], Medina-Pagola, J.E.[José E.],
An algorithm based on density and compactness for dynamic overlapping clustering,
PR(46), No. 11, November 2013, pp. 3040-3055.
Elsevier DOI 1306
Data mining; Clustering; Overlapping clustering algorithms; Dynamic clustering algorithms BibRef

Zhang, W.[Wei], Zhao, D.L.[De-Li], Wang, X.G.[Xiao-Gang],
Agglomerative clustering via maximum incremental path integral,
PR(46), No. 11, November 2013, pp. 3056-3065.
Elsevier DOI 1306
Agglomerative clustering; Path integral; Graph algorithms; Random walk BibRef

Kim, B.S.[Byung-Soo], Park, J.Y.[Jae Young], Gilbert, A.C.[Anna C.], Savarese, S.[Silvio],
Hierarchical classification of images by sparse approximation,
IVC(31), No. 12, 2013, pp. 982-991.
Elsevier DOI 1312
Sparse approximation BibRef

Kim, B.S.[Byung Soo], Park, J.Y.[Jae Young], Mohan, A.[Anush], Gilbert, A.C.[Anna C.], Savarese, S.[Silvio],
Hierarchical Classification of Images by Sparse Approximation 1,
BMVC11(xx-yy).
HTML Version. 1110
BibRef

Abin, A.A.[Ahmad Ali], Beigy, H.[Hamid],
Active selection of clustering constraints: a sequential approach,
PR(47), No. 3, 2014, pp. 1443-1458.
Elsevier DOI 1312
Active constraint selection BibRef

Abin, A.A.[Ahmad Ali], Beigy, H.[Hamid],
Active constrained fuzzy clustering: A multiple kernels learning approach,
PR(48), No. 3, 2015, pp. 953-967.
Elsevier DOI 1412
Constrained clustering BibRef

Lotfi, A.[Abdulrahman], Moradi, P.[Parham], Beigy, H.[Hamid],
Density peaks clustering based on density backbone and fuzzy neighborhood,
PR(107), 2020, pp. 107449.
Elsevier DOI 2008
Fuzzy kernel, Density peaks clustering, Noise detection, Label propagation BibRef

Abin, A.A.[Ahmad Ali],
Clustering with side information: Further efforts to improve efficiency,
PRL(84), No. 1, 2016, pp. 252-258.
Elsevier DOI 1612
Constrained clustering BibRef

Sharmila, T.S.[T. Sree], Ramar, K., Raja, T.S.R.[T. Sree Renga],
Impact of applying pre-processing techniques for improving classification accuracy,
SIViP(8), No. 1, January 2014, pp. 149-157.
WWW Link. 1402
BibRef

Zhu, S.[Shiai], Wei, X.Y.[Xiao-Yong], Ngo, C.W.[Chong-Wah],
Collaborative error reduction for hierarchical classification,
CVIU(124), No. 1, 2014, pp. 79-90.
Elsevier DOI 1406
Concept detection BibRef

Huang, X., Lu, Q., Zhang, L., Plaza, A.,
New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study,
GeoRS(52), No. 11, November 2014, pp. 7140-7159.
IEEE DOI 1407
Anisotropic magnetoresistance BibRef

Huang, S.J.[Sheng-Jun], Jin, R.[Rong], Zhou, Z.H.[Zhi-Hua],
Active Learning by Querying Informative and Representative Examples,
PAMI(36), No. 10, October 2014, pp. 1936-1949.
IEEE DOI 1410
learning (artificial intelligence) BibRef

Espinola, M., Piedra-Fernandez, J.A., Ayala, R., Iribarne, L., Wang, J.Z.,
Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata,
GeoRS(53), No. 2, February 2015, pp. 795-809.
IEEE DOI 1411
cellular automata BibRef

de Morsier, F.[Frank], Tuia, D.[Devis], Borgeaud, M.[Maurice], Gass, V.[Volker], Thiran, J.P.[Jean-Philippe],
Cluster validity measure and merging system for hierarchical clustering considering outliers,
PR(48), No. 4, 2015, pp. 1478-1489.
Elsevier DOI 1502
Clustering BibRef

de Morsier, F.[Frank], Borgeaud, M.[Maurice], Gass, V.[Volker], Thiran, J.P.[Jean-Philippe], Tuia, D.[Devis],
Kernel Low-Rank and Sparse Graph for Unsupervised and Semi-Supervised Classification of Hyperspectral Images,
GeoRS(54), No. 6, June 2016, pp. 3410-3420.
IEEE DOI 1606
hyperspectral imaging BibRef

Leski, J.M.[Jacek M.], Kotas, M.[Marian],
Hierarchical clustering with planar segments as prototypes,
PRL(54), No. 1, 2015, pp. 1-10.
Elsevier DOI 1502
Hierarchical clustering BibRef

Mall, R.[Raghvendra], Mehrkanoon, S.[Siamak], Suykens, J.A.K.[Johan A.K.],
Identifying intervals for hierarchical clustering using the Gershgorin circle theorem,
PRL(55), No. 1, 2015, pp. 1-7.
Elsevier DOI 1503
Gershgorin circle theorem BibRef

Mehrkanoon, S.[Siamak], Huang, X.L.[Xiao-Lin], Suykens, J.A.K.[Johan A.K.],
Indefinite kernel spectral learning,
PR(78), 2018, pp. 144-153.
Elsevier DOI 1804
Semi-supervised learning, Scalable models, Indefinite kernels, Kernel spectral clustering, Low embedding dimension BibRef

He, M.Z.[Ming-Zhen], He, F.[Fan], Shi, L.[Lei], Huang, X.L.[Xiao-Lin], Suykens, J.A.K.[Johan A. K.],
Learning With Asymmetric Kernels: Least Squares and Feature Interpretation,
PAMI(45), No. 8, August 2023, pp. 10044-10054.
IEEE DOI 2307
Kernel, Support vector machines, Directed graphs, Task analysis, Feature extraction, Matrix decomposition, Symmetric matrices, least squares support vector machine BibRef

Lian, C.F.[Chun-Feng], Ruan, S.[Su], Denœux, T.[Thierry],
An evidential classifier based on feature selection and two-step classification strategy,
PR(48), No. 7, 2015, pp. 2318-2327.
Elsevier DOI 1504
Dempster-Shafer theory BibRef

Zhong, C.M.[Cai-Ming], Yue, X.D.[Xiao-Dong], Lei, J.S.[Jing-Sheng],
Visual hierarchical cluster structure: A refined co-association matrix based visual assessment of cluster tendency,
PRL(59), No. 1, 2015, pp. 48-55.
Elsevier DOI 1505
Hierarchical clustering BibRef

Gillis, N., Kuang, D.[Da], Park, H.S.[Hae-Sun],
Hierarchical Clustering of Hyperspectral Images Using Rank-Two Nonnegative Matrix Factorization,
GeoRS(53), No. 4, April 2015, pp. 2066-2078.
IEEE DOI 1502
feature extraction BibRef

Najjar, A.[Alameen], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
Bregman pooling: feature-space local pooling for image classification,
MultInfoRetr(4), No. 4, December 2015, pp. 247-259.
Springer DOI 1511
BibRef

Bakoben, M.[Maha], Bellotti, A.[Anthony], Adams, N.M.[Niall M.],
Improving clustering performance by incorporating uncertainty,
PRL(77), No. 1, 2016, pp. 28-34.
Elsevier DOI 1606
Clustering with uncertainty BibRef

Wu, H.[Hang], Liu, B.Z.[Bao-Zhen], Su, W.H.[Wei-Hua], Zhang, W.C.[Wen-Chang], Sun, J.G.[Jing-Gong],
Hierarchical Coding Vectors for Scene Level Land-Use Classification,
RS(8), No. 5, 2016, pp. 436.
DOI Link 1606
BibRef

Alsahwa, B.[Bassem], Solaiman, B.[Basel], Almouahed, S.[Shaban], Bossé, É.[Éloi], Guériot, D.[Didier],
Iterative Refinement of Possibility Distributions by Learning for Pixel-Based Classification,
IP(25), No. 8, August 2016, pp. 3533-3545.
IEEE DOI 1608
Markov processes BibRef

Zhuo, Z.L.[Zhong-Liu], Zhang, X.S.[Xiao-Song], Niu, W.[Weina], Yang, G.W.[Guo-Wu], Zhang, J.Z.[Jing-Zhong],
Improving data field hierarchical clustering using Barnes-Hut algorithm,
PRL(80), No. 1, 2016, pp. 113-120.
Elsevier DOI 1609
Barnes-Hut algorithm BibRef

Hoyoux, T.[Thomas], Rodríguez-Sánchez, A.J.[Antonio J.], Piater, J.H.[Justus H.],
Can Computer Vision Problems Benefit from Structured Hierarchical Classification?,
MVA(27), No. 8, November 2016, pp. 1299-1312.
Springer DOI 1612
BibRef
Earlier: Add A4: Szedmak, S.[Sandor], CAIP15(II:403-414).
Springer DOI 1511
BibRef

Li, S.N.[Shao-Ning], Li, W.J.[Wen-Jing], Qiu, J.[Jia],
A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Yang, C.X.[Chen-Xue], Ye, M.[Mao], Tang, S.[Song], Xiang, T.[Tao], Liu, Z.J.[Zi-Jian],
Semi-supervised low-rank representation for image classification,
SIViP(11), No. 1, January 2017, pp. 73-80.
Springer DOI 1702
BibRef

Cheng, F.Y.[Fei-Yang], He, X.M.[Xu-Ming], Zhang, H.[Hong],
Stacked Learning to Search for Scene Labeling,
IP(26), No. 4, April 2017, pp. 1887-1898.
IEEE DOI 1704
Cost function BibRef

Bertini Junior, J.R.[João Roberto], do Carmo Nicoletti, M.[Maria],
Enhancing classification performance using attribute-oriented functionally expanded data,
PRL(89), No. 1, 2017, pp. 39-45.
Elsevier DOI 1704
Improving classification performance BibRef

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Scaling-up multiobjective evolutionary clustering algorithms using stratification,
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Elsevier DOI 1706
Multiobjective evolutionary algorithms BibRef

Wang, L.G.[Lei-Guang], Huang, X.[Xin], Zheng, C.[Chen], Zhang, Y.[Yun],
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PandRS(128), No. 1, 2017, pp. 223-239.
Elsevier DOI 1706
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Mondal, S.A.[Sakib A.],
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Elsevier DOI 1804
Clustering, Hierarchical, Approximation BibRef

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MultMed(21), No. 4, April 2019, pp. 809-820.
IEEE DOI 1903
Visualization, Solid modeling, Feature extraction, Videos, two-stage clustering BibRef

Oh, K.W.[Ki-Won], Choi, K.S.[Kang-Sun],
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RealTimeIP(16), No. 4, August 2019, pp. 945-956.
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Whang, J.J.Y.[Joyce Ji-Young], Hou, Y.Y.[Yang-Yang], Gleich, D.F.[David F.], Dhillon, I.S.[Inderjit S.],
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PAMI(41), No. 11, November 2019, pp. 2644-2659.
IEEE DOI 1910
Clustering algorithms, Linear programming, Kernel, Iterative methods, Computer science, Anomaly detection, community detection BibRef

Zhang, J.[Jun], Zhang, M.[Min], Shi, L.[Lukui], Yan, W.J.[Wen-Jie], Pan, B.[Bin],
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Huang, L.[Lei], Ma, Y.Q.[Yu-Qing], Liu, X.L.[Xiang-Long],
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Elsevier DOI 2002
Active learning, Multi-class classification, Label propagation, Non-parametric BibRef

Lamb, D.S.[David S.], Downs, J.[Joni], Reader, S.[Steven],
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Hao, X.H.[Xiao-Hui], Wu, Y.[Yiquan], Wang, P.[Peng],
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Lin, Q.F.[Qi-Feng], Ling, Q.[Qing],
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SPLetters(28), 2021, pp. 723-727.
IEEE DOI 2105
Convergence, Signal processing algorithms, Function approximation, Approximation algorithms, Acceleration, gradient tracking BibRef

Li, M.[Ming], Lei, L.[Lin], Tang, Y.Q.[Yu-Qi], Sun, Y.[Yuli], Kuang, G.Y.[Gang-Yao],
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Xie, W.B.[Wen-Bo], Liu, Z.[Zhen], Das, D.[Debarati], Chen, B.[Bin], Srivastava, J.[Jaideep],
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Elsevier DOI 2301
Hierarchical clustering, Election tree, Representative node, Root BibRef

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Elsevier DOI 2301
Hierarchical multi-class classification, Multi-class classification, Class hierarchies BibRef

Han, X.[Xin], Zhu, Y.[Ye], Ting, K.M.[Kai Ming], Li, G.[Gang],
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Elsevier DOI 2304
Agglomerative hierarchical clustering, Varied densities, Dendrogram purity, Isolation kernel, Gaussian kernel BibRef

Hao, P.Y.[Peng-Yi], Shi, K.J.[Kang-Jian], Tian, S.Y.[Shu-Yuan], Wu, F.[Fuli],
Uncertainty-aware iterative learning for noisy-labeled medical image segmentation,
IET-IPR(17), No. 13, 2023, pp. 3830-3840.
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Zhao, H.H.Y.[Henry Heng-Yuan], Wang, P.[Pichao], Zhao, Y.Y.[Yu-Yang], Luo, H.[Hao], Wang, F.[Fan], Shou, M.Z.[Mike Zheng],
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Springer DOI 2402
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Zhao, Z.Y.[Zi-Yu], Gan, L.L.[Lei-Lei], Shen, T.[Tao], Kuang, K.[Kun], Wu, F.[Fei],
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Hierarchical multi-granularity classification, Causal inference BibRef

Chaudhuri, U.[Ushasi], Chaudhuri, S.[Syomantak], Chaudhuri, S.[Subhasis],
GuCNet: A Guided Clustering-based Network for Improved Classification,
ICPR21(7335-7342)
IEEE DOI 2105
Training, Semantics, Neural networks, Prototypes, Network architecture, Benchmark testing, Topology BibRef

Chen, C.[Chao], Li, G.B.[Guan-Bin], Xu, R.J.[Rui-Jia], Chen, T.S.[Tian-Shui], Wang, M.[Meng], Lin, L.[Liang],
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IEEE DOI 2002
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Montaldo, A., Fronda, L., Hedhli, I., Moser, G., Serpico, S.B., Zerubia, J.,
Causal Markov Mesh Hierarchical Modeling for the Contextual Classification of Multiresolution Satellite Images,
ICIP19(2716-2720)
IEEE DOI 1910
Multiresolution images, causality, hierarchical Markov random field, Markov mesh random field, semantic image segmentation BibRef

Saha, S.[Soham], Varma, G.[Girish], Jawahar, C.V.,
Class2Str: End to End Latent Hierarchy Learning,
ICPR18(1000-1005)
IEEE DOI 1812
Feature extraction, Training, Encoding, Entropy, Neural networks, Binary trees, Testing BibRef

José-García, A.[Adán], Gómez-Flores, W.[Wilfrido],
Evolutionary Clustering Using Multi-prototype Representation and Connectivity Criterion,
MCPR17(63-73).
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Karray, E., Loghmari, M.A., Naceur, M.S.,
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ISIVC16(236-241)
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Hyperspectral imaging BibRef

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DICTA16(1-7)
IEEE DOI 1701
nonnegative matrix factorization. Clustering methods BibRef

Hedhli, I.[Ihsen], Moser, G.[Gabriele], Serpico, S.B.[Sebastiano B.], Zerubia, J.B.[Josiane B.],
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ICIP16(1349-1353)
IEEE DOI 1610
Analytical models BibRef

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DICTA15(1-8)
IEEE DOI 1603
computational complexity BibRef

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MultiTemp15(1-4)
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Big Data BibRef

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ICPR14(3720-3725)
IEEE DOI 1412
Accuracy BibRef

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ICPR16(2568-2573)
IEEE DOI 1705
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eigenvalues and eigenfunctions BibRef

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