14.2.2 Clustering, Classification, General Methods

Chapter Contents (Back)
Clustering.
See also Density Based Clustering.

Cramér, H.,
Mathematical Methods of Statistics,
PrincetonUniversity Press, Princeton, NJ, 1946. The mean-squared error for any estimate of a nonrandom parameter has a lower bound, the Cramér-Rao Bound, which defines the ultimate accuracy of any estimation procedure. This lower bound is intimately related to the maximum likelihood estimator. BibRef 4600

Bonner, R.E.,
On Some Clustering Techniques,
IBMRD(8), January 1964, pp. 22-31. BibRef 6401

Jain, A.K., Murty, M.N., and Flynn, P.J.,
Data clustering: A review,
Surveys(31), 1999, No. 3, pp. 264-323.
DOI Link Survey, Pattern Recognition. BibRef 9900

Jain, A.K.[Anil K.],
Data clustering: 50 years beyond K-means,
PRL(31), No. 8, 1 June 2010, pp. 651-666.
Elsevier DOI 1004
Survey, Clustering. Award, PRL Most Cited. 2019-2011 Award, PRL Most Cited. 2010-2012 Data clustering; User's dilemma; Historical developments; Perspectives on clustering; King-Sun Fu prize BibRef

Forgey, E.,
Cluster Analysis of Multivariate Data: Efficiency vs. Interpretability of Classification,
Biometrics(21), 1965, pp. 768. BibRef 6500

Nahi, N.E.,
Estimation Theory and Applications,
John Wileyand Sons Inc., 1969. BibRef 6900

Jain, A.K.,
Cluster Analysis,
HPRIP86(33-57). BibRef 8600

Jain, A.K., and Dubes, R.C.,
Algorithms for Clustering Data,
Prentice HallInc., Englewood Cliffs, NJ 1988. ISBN 0-13-022278-X.
PDF File. BibRef 8800

Dubes, R.C.,
Cluster Analysis and Related Issues,
HPRCV97(Chapter I:1). (Michigan State Univ.). BibRef 9700

Dubes, R.C.[Richard C.], Jain, A.K.[Anil K.],
Clustering techniques: The user's dilemma,
PR(8), No. 4, October 1976, pp. 247-260.
Elsevier DOI 0309
BibRef

Dubes, R.C.[Richard C.], Jain, A.K.[Anil K.],
Validity studies in clustering methodologies,
PR(11), No. 4, 1979, pp. 235-254.
Elsevier DOI 0309
BibRef

Bailey, Jr., T.A.[Thomas A.], Dubes, R.C.[Richard C.],
Cluster validity profiles,
PR(15), No. 2, 1982, pp. 61-83.
Elsevier DOI 0309
BibRef

Panayirci, E.[Erdal], Dubes, R.C.[Richard C.],
A test for multidimensional clustering tendency,
PR(16), No. 4, 1983, pp. 433-444.
Elsevier DOI BibRef 8300
Earlier: Abstract: PR(16), No. 3, 1983, pp. Page 357.
Elsevier DOI 0309
BibRef

Kaminuma, T., Takekawa, T., Watanabe, S.,
Reduction of clustering problem to pattern recognition,
PR(1), No. 3, March 1969, pp. 195-205.
Elsevier DOI 0309
BibRef

Freeman, J.J.,
Experiments in discrimination and classification,
PR(1), No. 3, March 1969, pp. 207-218.
Elsevier DOI 0309
BibRef

Holdermann, F.,
Classification by cascaded threshold elements,
PR(3), No. 3, October 1971, pp. 243-251.
Elsevier DOI 0309
BibRef

Diday, E.[Edwin],
Optimization in non-hierarchical clustering,
PR(6), No. 1, June 1974, pp. 17-33.
Elsevier DOI 0309
Biological profiles. Minerals. BibRef

Diday, E.[Edwin], Cucumel, G.,
Compatibility and consensus in numerical taxonomy,
ICPR88(II: 1059-1061).
IEEE DOI 8811
BibRef

Coray, G., Noetzel, A., Selkow, S.M.,
Order independence in local clustering algorithms,
CGIP(4), No. 2, June 1975, pp. 120-132.
Elsevier DOI 0501
BibRef

Slagle, J.R., Chang, C.L., Heller, S.R.,
A Clustering and Data Reorganizing Algorithm,
SMC(5), 1975, pp. 125-128. BibRef 7500

Yau, S.S., Chang, S.C.,
A direct method for cluster analysis,
PR(7), No. 4, December 1975, pp. 215-224.
Elsevier DOI 0309
BibRef

Kittler, J.V.,
A locally sensitive method for cluster analysis,
PR(8), No. 1, January 1976, pp. 23-33.
Elsevier DOI 0309
BibRef

Apostolico, A., Caianiello, E.R., Fischetti, E., Vitulano, S.,
C-calculus: An elementary approach to some problems in pattern recognition,
PR(10), No. 5-6, 1978, pp. 375-387.
Elsevier DOI 0309
BibRef

Wong, A.K.C., and Wang, D.C.C.,
DECA: A Discrete-Valued Data Clustering Algorithm,
PAMI(1), No. 4, October 1979, 342-349. BibRef 7910

Chittineni, C.B.,
Utilization of Spectral-Spatial Information in the Classification of Imagery Data,
CGIP(16), No. 4, August 1981, pp. 305-340.
Elsevier DOI BibRef 8108

Chittineni, C.B.,
Some approaches to optimal cluster labeling with applications to remote sensing,
PR(15), No. 3, 1982, pp. 201-216.
Elsevier DOI 0309
BibRef

Chittineni, C.B.,
Signature extension in remote sensing,
PR(12), No. 4, 1980, pp. 243-249.
Elsevier DOI 0309
BibRef

Massart, D.L.[Désiré L.], Plastria, F.[Frank], Kaufman, L.[Leonard],
Non-hierarchical clustering with MASLOC,
PR(16), No. 5, 1983, pp. 507-516.
Elsevier DOI 0309
based on the p-median model of location theory. BibRef

Lowitz, G.E.,
What the Fourier Transform Can Really Bring to Clustering,
PR(17), No. 6, 1984, pp. 657-665.
Elsevier DOI
See also What a Histogram Can Really Tell the Classifier. BibRef 8400

Kusiak, A.[Andrew],
Analysis of integer programming formulations of clustering problems,
IVC(2), No. 1, February 1984, pp. 35-40.
Elsevier DOI 0401
BibRef

Dehne, F.,
Optical Clustering,
VC(2), 1986, pp. 39-43. BibRef 8600

Kim, J.H.,
Distributed Inference for Plausible Classification,
PRL(5), 1987, pp. 195-201. BibRef 8700

Jain, A.K., Moreau, J.V.,
Bootstrap technique in cluster analysis,
PR(20), No. 5, 1987, pp. 547-568.
Elsevier DOI 0309
BibRef

Umesh, R.M.,
A technique for cluster formation,
PR(21), No. 4, 1988, pp. 393-400.
Elsevier DOI 0309
BibRef

Bryant, J.[Jack],
A fast classifier for image data,
PR(22), No. 1, 1989, pp. 45-48.
Elsevier DOI 0309
BibRef

Shekar, B., Murty, M.N.[M. Narasimha], Krishna, G.,
Structural aspects of semantic-directed clusters,
PR(22), No. 1, 1989, pp. 65-74.
Elsevier DOI 0309
BibRef

Liu, S.T.[Song-Tyang], Tsai, W.H.[Wen-Hsiang],
Moment-preserving clustering,
PR(22), No. 4, 1989, pp. 433-447.
Elsevier DOI 0309
BibRef

Postaire, J.G., Touzani, A.,
Mode boundary detection by relaxation for cluster analysis,
PR(22), No. 5, 1989, pp. 477-489.
Elsevier DOI 0309
BibRef

Wilson, R., Spann, M.,
A new approach to clustering,
PR(23), No. 12, 1990, pp. 1413-1425.
Elsevier DOI 0401
BibRef

Kaneko, K.[Kunihiko],
Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements,
PhysicaD(41), No. 2, March 1990, Pages 137-172.
Elsevier DOI BibRef 9003

Valev, V.[Ventzeslav], Zhuravlev, J.I.[Jurey Ivanovich],
Integer-valued problems of transforming the training tables in k-valued code in pattern recognition problems,
PR(24), No. 4, 1991, pp. 283-288.
Elsevier DOI 0401
BibRef

Rose, K., Gurewitz, E., Fox, G.C.,
Constrained clustering as an optimization method,
PAMI(15), No. 8, August 1993, pp. 785-794.
IEEE DOI 0401
BibRef

Postaire, J.G., Zhang, R.D., Lecocq-Botte, C.,
Cluster analysis by binary morphology,
PAMI(15), No. 2, February 1993, pp. 170-180.
IEEE DOI 0401
BibRef

Lakroum, S., Devlaminck, V., Terrier, P., Biela-Enberg, P., Postaire, J.G.,
Clustering of The Poincaré Vectors,
ICIP05(II: 1190-1193).
IEEE DOI 0512
BibRef

Lin, J.C., Tsai, W.H.,
Feature-Preserving Clustering of 2-D Data for 2-Class Problems Using Analytical Formulas: An Automatic and Fast Approach,
PAMI(16), No. 5, May 1994, pp. 554-560.
IEEE DOI BibRef 9405

Ramdas, V., Sridhar, V., Krishna, G.,
An Effective Clustering Technique for Feature-Extraction,
PRL(15), No. 9, September 1994, pp. 885-891. BibRef 9409

Pellegretti, P., Roli, F., Serpico, S.B., Vernazza, G.,
Supervised learning of descriptions for image recognition purposes,
PAMI(16), No. 1, January 1994, pp. 92-98.
IEEE DOI 0401
BibRef

Cubanski, D., Cyganski, D.,
Multivariate Classification through Adaptive Delaunay-Based C-0 Spline Approximation,
PAMI(17), No. 4, April 1995, pp. 403-417.
IEEE DOI BibRef 9504

Osbourn, G.C., Martinez, R.F.,
Empirically defined regions of influence for clustering analyses,
PR(28), No. 11, November 1995, pp. 1793-1806.
Elsevier DOI 0401
Use human cluster judgments as the cluster criteria. BibRef

McLachlan, G.J., Krishnan, T.,
The EM Algorithm and Extensions,
John Wiley& Sons, 1996. BibRef 9600

Pei, S.C.[Soo-Chang], Cheng, C.M.[Ching-Min],
A Fast 2-Class Classifier for 2D Data Using Complex-Moment-Preserving Principle,
PR(29), No. 3, March 1996, pp. 519-531.
Elsevier DOI BibRef 9603

Kim, J.W., Krishnapuram, R., Dave, R.,
Application of the Least Trimmed Squares Technique to Prototype-Based Clustering,
PRL(17), No. 6, May 15 1996, pp. 633-641. 9607
BibRef

Lin, J.C.[Ja-Chen], Lin, W.J.[Wu-Ja],
Real-Time And Automatic 2-Class Clustering By Analytical Formulas,
PR(29), No. 11, November 1996, pp. 1919-1930.
Elsevier DOI 9612
BibRef

Wharton, S.W.[Stephen W.],
A Generalized Histogram Clustering Scheme for Multidimensional Image Data,
PR(16), No. 2, 1983, pp. 193-199.
Elsevier DOI 9611
BibRef

Li, Q., Tufts, D.W.,
Principal Feature Classification,
TNN(8), No. 1, January 1997, pp. 155-160. 9701
BibRef

Murtagh, F.,
Contiguity-Constrained Clustering for Image Analysis,
PRL(13), 1992, pp. 677-683. BibRef 9200

Doncarli, C., and Carpentier, E.L.,
An Optimal Approach for Random Signals Classification,
PAMI(13), No. 11, November 1991, pp. 1192-1196.
IEEE DOI BibRef 9111

Boberg, J.[Jorma], Salakoski, T.[Tapio],
Representative Noise-Free Complete-Link Classification with Application to Protein Structures,
PR(30), No. 3, March 1997, pp. 467-482.
Elsevier DOI 9705
BibRef

Velthuizen, R.P.[Robert P.], Hall, L.O.[Lawrence O.], Clarke, L.P.[Laurence P.], Silbiger, M.L.[Martin L.],
An Investigation of Mountain Method Clustering for Large Data Sets,
PR(30), No. 7, July 1997, pp. 1121-1135.
Elsevier DOI 9707
BibRef

Ha, T.M.,
The Optimum Class-Selective Rejection Rule,
PAMI(19), No. 6, June 1997, pp. 608-615.
IEEE DOI 9708
BibRef
Earlier:
An Optimum Class-Selective Rejection Rule for Pattern Recognition,
ICPR96(II: 75-80).
IEEE DOI 9608
(Univ. of Berne, CH) BibRef

Cheung, J., Ferris, D., Kurz, L.,
On Classification of Multispectral Infrared Image Data,
IP(6), No. 10, October 1997, pp. 1456-1460.
IEEE DOI 9710
BibRef

Kastella, K.,
Discrimination Gain to Optimize Detection and Classification,
SMC-A(27), No. 1, January 1997, pp. 112-116.
IEEE Top Reference. 9701
BibRef

Muzzolini, R.[Russell], Yang, Y.H.[Yee-Hong], Pierson, R.[Roger],
Classifier Design with Incomplete Knowledge,
PR(31), No. 4, April 1998, pp. 345-369.
Elsevier DOI 9803
BibRef

Erol, H., Akdeniz, F.,
A New Supervised Classification Method for Quantitative-Analysis of Remotely Sensed Multispectral Data,
JRS(19), No. 4, March 10 1998, pp. 775-782. 9803
BibRef

Mandal, D.P.[Deba Prasad],
Partitioning of Feature Space for Pattern Classification,
PR(30), No. 12, December 1997, pp. 1971-1990.
Elsevier DOI 9805
BibRef

Zhu, Q.M., Cai, Y.,
A Subclass Model for Nonlinear Pattern Classification,
PRL(19), No. 1, January 1998, pp. 19-29. 9807
BibRef

Sukanya, P.[Phongsuphap], Takamatsu, R.[Ryo], Sato, M.[Makoto],
The Surface Shape Operator and Multiscale Approach for Image Classification,
IEICE(E81-A), No. 8, August 1998, pp. 1683-1689. 9809
BibRef
Earlier:
Image Classification Using the Surface-shape Operator and Multiscale Features,
ICPR98(Vol I: 334-337).
IEEE DOI 9808
BibRef
Earlier:
The surface-shape operator based shading-tolerant method for multiscale image analysis,
ICIP98(I: 221-225).
IEEE DOI 9810
BibRef

Judd, D., McKinley, P.K., and Jain, A.K.,
Large-Scale Parallel Data Clustering,
PAMI(20), No. 8, August 1998, pp. 871-876.
IEEE DOI BibRef 9808
Earlier: ICPR96(IV: 488-493).
IEEE DOI 9608
Parallel Algorithms. (Michigan State Univ., USA) Reduce mean-square-error in data clustering. Use a network of workstations. Apply to texture segmentation. BibRef

Hogg, T.[Trevor], Talhami, H.[Habib], Rees, D.[David],
An improved synergetic algorithm for image classification,
PR(31), No. 12, December 1998, pp. 1893-1903.
Elsevier DOI BibRef 9812

Chaudhuri, B.B., Bhowmik, P.R.,
An approach of clustering data with noisy or imprecise feature measurement,
PRL(19), No. 14, December 1998, pp. 1307-1317. BibRef 9812

Baram, Y.[Yoram],
A geometric approach to consistent classification,
PR(33), No. 2, February 2000, pp. 177-184.
Elsevier DOI 0001
BibRef

Ichino, M.[Manabu], Yaguchi, H.[Hiroyuki],
An apparent simplicity appearing in pattern classification problems,
PR(33), No. 9, September 2000, pp. 1467-1474.
Elsevier DOI 0005
BibRef

Wong, C.C.[Ching-Chang], Chen, C.C.[Chia-Chong], Su, M.C.[Mu-Chun],
A novel algorithm for data clustering,
PR(34), No. 2, February 2001, pp. 425-442.
Elsevier DOI 0011
BibRef

Chau, T.[Tom],
Marginal Maximum Entropy Partitioning Yields Asymptotically Consistent Probability Density Functions,
PAMI(23), No. 4, April 2001, pp. 414-417.
IEEE DOI 0104
BibRef

Kim, D.J.[Dai-Jin],
Data classification based on tolerant rough set,
PR(34), No. 8, August 2001, pp. 1613-1624.
Elsevier DOI 0105
BibRef

Wang, J.H., Rau, J.D.,
VQ-agglomeration: a novel approach to clustering,
VISP(148), No. 1, February 2001, pp. 36-44. 0105
BibRef

Ananthanarayana, V.S., Murty, M.N., Subramanian, D.K.,
An incremental data mining algorithm for compact realization of prototypes,
PR(34), No. 11, November 2001, pp. 2249-2251.
Elsevier DOI 0108
BibRef

Kuncheva, L.I.[Ludmila I.], Kountchev, R.K.[Roumen K.],
Generating classifier outputs of fixed accuracy and diversity,
PRL(23), No. 5, March 2002, pp. 593-600.
Elsevier DOI 0202
BibRef

Boutsinas, B., Gnardellis, T.,
On distributing the clustering process,
PRL(23), No. 8, June 2002, pp. 999-1008.
Elsevier DOI 0204
BibRef

Schöll, J.[Joachim], Schöll-Paschinger, E.[Elisabeth],
Classification by restricted random walks,
PR(36), No. 6, June 2003, pp. 1279-1290.
Elsevier DOI 0304
BibRef

Leski, J.K.[Jacek K.],
Ho-Kashyap classifier with generalization control,
PRL(24), No. 14, October 2003, pp. 2281-2290.
Elsevier DOI 0307

See also Algorithm for Linear Inequalities and its Applications, An. BibRef

Largeron-Leténo, C.[Christine],
Prediction suffix trees for supervised classification of sequences,
PRL(24), No. 16, December 2003, pp. 3153-3164.
Elsevier DOI 0310
DNA sequences. BibRef

Bandyopadhyay, S.[Sanghamitra],
An automatic shape independent clustering technique,
PR(37), No. 1, January 2004, pp. 33-45.
Elsevier DOI 0311
BibRef

Ma, E.W.M.[Eden W.M.], Chow, T.W.S.[Tommy W. S.],
A new shifting grid clustering algorithm,
PR(37), No. 3, March 2004, pp. 503-514.
Elsevier DOI 0401
BibRef

Lee, E.W.M., Lim, C.P., Yuen, R.K.K., Lo, S.M.,
A Hybrid Neural Network Model for Noisy Data Regression,
SMC-B(34), No. 2, April 2004, pp. 951-960.
IEEE Abstract. 0404
BibRef

Diatta, J.[Jean],
A relation between the theory of formal concepts and multiway clustering,
PRL(25), No. 10, 16 July 2004, pp. 1183-1189.
Elsevier DOI 0407
BibRef

Kim, D.W.[Dae-Won], Lee, K.Y.[Ki-Young], Lee, D.[Doheon], Lee, K.H.[Kwang H.],
A kernel-based subtractive clustering method,
PRL(26), No. 7, 15 May 2005, pp. 879-891.
Elsevier DOI 0506
Use kernel distance rather than sum of squares. BibRef

Zheng, L.Y.[Li-Ying], Tang, X.L.[Xiang-Long],
A new parameter control method for S-GCM,
PRL(26), No. 7, 15 May 2005, pp. 939-942.
Elsevier DOI 0506
Globally coupled map. See Kaneko (
See also Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements. ), and Ishii (
See also net work of chaotic elements for information processing, A. ) BibRef

Chang, K.C., Yeh, M.F.,
Grey relational analysis based approach for data clustering,
VISP(152), No. 2, April 2005, pp. 165-172.
DOI Link 0510
BibRef

Blekas, K., Lagaris, I.E.,
Newtonian clustering: An approach based on molecular dynamics and global optimization,
PR(40), No. 6, June 2007, pp. 1734-1744.
Elsevier DOI 0704
Clustering; Molecular dynamics; Global optimization; Order statistics BibRef

Culp, M.[Mark], Michailidis, G.[George],
Graph-Based Semisupervised Learning,
PAMI(30), No. 1, January 2008, pp. 174-179.
IEEE DOI 0711
BibRef

Cazzanti, L.[Luca], Gupta, M.R.[Maya R.], Koppal, A.J.[Anjali J.],
Generative models for similarity-based classification,
PR(41), No. 7, July 2008, pp. 2289-2297.
Elsevier DOI 0804
Similarity; Maximum entropy; Discriminant analysis BibRef

Yue, S.H.[Shi-Hong], Wei, M.M.[Miao-Miao], Wang, J.S.[Jeen-Shing], Wang, H.X.[Hua-Xiang],
A general grid-clustering approach,
PRL(29), No. 9, 1 July 2008, pp. 1372-1384.
Elsevier DOI 0711
Clustering; Core grid; Grid size; Locality BibRef

Raghuraj, R.[Rao], Lakshminarayanan, S.[Samavedham],
Variable predictive models: A new multivariate classification approach for pattern recognition applications,
PR(42), No. 1, January 2009, pp. 7-16.
Elsevier DOI 0809
Data classification; Variable predictive models; Discriminant analysis; Machine learning; Multivariate statistics BibRef

Grall-Maes, E.[Edith], Beauseroy, P.[Pierre],
Optimal Decision Rule with Class-Selective Rejection and Performance Constraints,
PAMI(31), No. 11, November 2009, pp. 2073-2082.
IEEE DOI 0910
Include cost to be minimized, and decision options in classifier design. BibRef

Jrad, N.[Nisrine], Grall-Maes, E.[Edith], Beauseroy, P.[Pierre],
Supervised learning rule selection for multiclass decision with performance constraints,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Raj, A.[Anil], Wiggins, C.H.[Chris H.],
An Information-Theoretic Derivation of Min-Cut-Based Clustering,
PAMI(32), No. 6, June 2010, pp. 988-995.
IEEE DOI 1004
Min-cut clustering, based on minimizing one of two heuristic cost functions proposed by Shi and Malik.
See also Normalized Cuts and Image Segmentation. Analyze to understand general applications. BibRef

Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San],
Quantization-based clustering algorithm,
PR(43), No. 8, August 2010, pp. 2698-2711.
Elsevier DOI 1006
BibRef
Earlier:
GCA: A real-time grid-based clustering algorithm for large data set,
ICPR06(II: 740-743).
IEEE DOI 0609
BibRef
Earlier:
Mining Uncertain Data in Low-dimensional Subspace,
ICPR06(II: 748-751).
IEEE DOI 0609
BibRef
And:
Genetic-based K-means algorithm for selection of feature variables,
ICPR06(II: 744-747).
IEEE DOI 0609
Histogram; Clustering algorithm; K-means BibRef

Canals, V.[Vincent], Morro, A.[Antoni], Rossello, J.L.[Josep L.],
Stochastic-based pattern-recognition analysis,
PRL(31), No. 15, 1 November 2010, pp. 2353-2356.
Elsevier DOI 1003
Stochastic logic; Pattern-recognition; Robotics navigation BibRef

Kawahara, Y.[Yoshinobu], Nagano, K.[Kiyohito], Okamoto, Y.[Yoshio],
Submodular fractional programming for balanced clustering,
PRL(32), No. 2, 15 January 2011, pp. 235-243.
Elsevier DOI 1101
Submodular function optimization; Balanced clustering; Discrete optimization BibRef

Sugihara, K.[Kokichi], Okabe, A.[Atsuyuki], Satoh, T.[Toshiaki],
Computational method for the point cluster analysis on networks,
GeoInfo(15), No. 1, January 2011, pp. 167-189.
WWW Link. 1102
BibRef

Li, J.L.[Jun-Lin], Fu, H.G.[Hong-Guang],
Molecular dynamics-like data clustering approach,
PR(44), No. 8, August 2011, pp. 1721-1737.
Elsevier DOI 1104
Molecular dynamics; Dynamics clustering; Data mining; Data clustering BibRef

Kwedlo, W.[Wojciech],
A clustering method combining differential evolution with the K-means algorithm,
PRL(32), No. 12, 1 September 2011, pp. 1613-1621.
Elsevier DOI 1108
Cluster analysis; Differential evolution; K-means algorithm BibRef

Krinidis, S.[Stelios], Krinidis, M.[Michail], Chatzis, V.,
Workspace for image clustering based on empirical mode decomposition,
IET-IPR(6), No. 6, 2012, pp. 778-785.
DOI Link 1210
empirical mode decomposition decompose into intrinsic mode functions. BibRef

Krinidis, S.[Stelios], Krinidis, M.[Michail],
Empirical mode decomposition on skeletonization pruning,
IVC(31), No. 8, August 2013, pp. 533-541.
Elsevier DOI 1306
Empirical mode decomposition; Ensemble empirical mode decomposition; Intrinsic mode; Skeleton; Skeletonization; Pruning BibRef

Sun, B., Wu, D.,
Self-Organizing-Queue Based Clustering,
SPLetters(19), No. 12, December 2012, pp. 902-905.
IEEE DOI 1212
BibRef

Esfahani, M.S.[Mohammad Shahrokh], Knight, J.[Jason], Zollanvari, A.[Amin], Yoon, B.J.[Byung-Jun], Dougherty, E.R.[Edward R.],
Classifier design given an uncertainty class of feature distributions via regularized maximum likelihood and the incorporation of biological pathway knowledge in steady-state phenotype classification,
PR(46), No. 10, October 2013, pp. 2783-2797.
Elsevier DOI 1306
Steady-state classifier; Biological-pathway knowledge; Uncertainty class; Regularized maximum-likelihood; Prior knowledge BibRef

Olszewski, D.[Dominik], Šter, B.[Branko],
Asymmetric clustering using the alpha-beta divergence,
PR(47), No. 5, 2014, pp. 2031-2041.
Elsevier DOI 1402
Clustering BibRef

Huang, H.B.[Hong-Bing], Huo, H.[Hong], Fang, T.[Tao],
Hierarchical Manifold Learning With Applications to Supervised Classification for High-Resolution Remotely Sensed Images,
GeoRS(52), No. 3, March 2014, pp. 1677-1692.
IEEE DOI 1403
geophysical image processing BibRef

Niu, D.L.[Dong-Lin], Dy, J.G.[Jennifer G.], Jordan, M.I.[Michael I.],
Iterative Discovery of Multiple Alternative Clustering Views,
PAMI(36), No. 7, July 2014, pp. 1340-1353.
IEEE DOI 1407
Algorithm design and analysis. Clusters that are clear in alternative projections. Circles, curves. BibRef

Chalmers, E.[Eric], Mizianty, M.[Marcin], Parent, E.[Eric], Yuan, Y.[Yan], Lou, E.[Edmond],
Toward maximum-predictive-value classification,
PR(47), No. 12, 2014, pp. 3949-3958.
Elsevier DOI 1410
Classification BibRef

Yang, S.M.[Shang-Ming], Yi, Z.[Zhang], He, X.F.[Xiao-Fei], Li, X.L.[Xue-Long],
A Class of Manifold Regularized Multiplicative Update Algorithms for Image Clustering,
IP(24), No. 12, December 2015, pp. 5302-5314.
IEEE DOI 1512
graph theory BibRef

Peng, H.[Hong], Wang, J.[Jun], Shi, P.[Peng], Riscos-Núńez, A.[Agustín], Pérez-Jiménez, M.J.[Mario J.],
An automatic clustering algorithm inspired by membrane computing,
PRL(68, Part 1), No. 1, 2015, pp. 34-40.
Elsevier DOI 1512
Membrane computing BibRef

Hong, S.H.[Seung-Hoon], Choi, J.H.[Jong-Hyun], Feyereisl, J.[Jan], Han, B.Y.[Boh-Yung], Davis, L.S.[Larry S.],
Joint Image Clustering and Labeling by Matrix Factorization,
PAMI(38), No. 7, July 2016, pp. 1411-1424.
IEEE DOI 1606
Clustering algorithms. Cluster and annotate set of images jointly. BibRef

Lee, Y.M.[Young-Min], Kwon, P.[Pil], Yu, K.[Kiyun], Park, W.[Woojin],
Method for Determining Appropriate Clustering Criteria of Location-Sensing Data,
IJGI(5), No. 9, 2016, pp. 151.
DOI Link 1610
From location-based services. BibRef

Tong, Q.H.[Qiu-Hui], Li, X.[Xiu], Yuan, B.[Bo],
A highly scalable clustering scheme using boundary information,
PRL(89), No. 1, 2017, pp. 1-7.
Elsevier DOI 1704
Clustering BibRef

Sadatnejad, K.[Khadijeh], Ghidary, S.S.[Saeed Shiry],
Adaptive spectrum transformation by topology preservation on indefinite proximity data,
PRL(98), No. 1, 2017, pp. 59-67.
Elsevier DOI 1710
Proximity data BibRef

Abe, S.[Shigeo],
Unconstrained large margin distribution machines,
PRL(98), No. 1, 2017, pp. 96-102.
Elsevier DOI 1710
Large margin distribution machines. trained by solving a set of linear equations. BibRef

Wang, B.J.[Bang-Jun], Zhang, L.[Li], Li, F.Z.[Fan-Zhang],
Supervised orthogonal discriminant projection based on double adjacency graphs for image classification,
IET-IPR(11), No. 11, November 2017, pp. 1050-1058.
DOI Link 1711
BibRef

Dimitropoulos, K.[Kosmas], Barmpoutis, P.[Panagiotis], Kitsikidis, A.[Alexandros], Grammalidis, N.[Nikos],
Classification of Multidimensional Time-Evolving Data Using Histograms of Grassmannian Points,
CirSysVideo(28), No. 4, April 2018, pp. 892-905.
IEEE DOI 1804
Autoregressive processes, Computational modeling, Data models, Hidden Markov models, Kernel, Manifolds, Tensile stress, multidimensional signal processing BibRef

Wu, T., Bajwa, W.U.,
A Low Tensor-Rank Representation Approach for Clustering of Imaging Data,
SPLetters(25), No. 8, August 2018, pp. 1196-1200.
IEEE DOI 1808
matrix algebra, pattern clustering, tensors, vectors, low tensor-rank representation approach, two-dimensional data, tensor multirank BibRef

Wu, T.[Tong],
Online Tensor Low-Rank Representation for Streaming Data Clustering,
CirSysVideo(33), No. 2, February 2023, pp. 602-617.
IEEE DOI 2302
Tensors, Optimization, Clustering algorithms, Matrix decomposition, Memory management, Iterative methods, Costs, tensor low-rank representation BibRef

Li, X.[Xi], Ma, H.M.[Hui-Min], Wang, X.[Xiang],
Feature proposal model on multidimensional data clustering and its application,
PRL(112), 2018, pp. 41-48.
Elsevier DOI 1809
Feature proposal, Feature non-maximum suppression, Grouped weighted clustering BibRef

Patel, N.[Nilesh], Tyagi, G.[Gaurav], Marcinek, P.[Pawel],
Random neighbourhood dynamic clustering,
IJCVR(8), No. 5, 2018, pp. 476-491.
DOI Link 1810
BibRef

Adolfsson, A.[Andreas], Ackerman, M.[Margareta], Brownstein, N.C.[Naomi C.],
To cluster, or not to cluster: An analysis of clusterability methods,
PR(88), 2019, pp. 13-26.
Elsevier DOI 1901
Clusterability, Cluster structure, Cluster tendency, Dimension reduction, Multimodality tests BibRef

Luchi, D.[Diego], Rodrigues, A.L.[Alexandre Loureiros], Varejăo, F.M.[Flávio Miguel],
Sampling approaches for applying DBSCAN to large datasets,
PRL(117), 2019, pp. 90-96.
Elsevier DOI 1901
Clustering, Sampling, DBSCAN BibRef

Qv, H.[Hui], Yin, J.[Jihao], Luo, X.Y.[Xiao-Yan],
LG: A clustering framework supported by point proximity relations,
PR(103), 2020, pp. 107265.
Elsevier DOI 2005
Two stages: Local Energy Gradient Oppression (LEGO) and the Guide Point Assignation (GPA). Clustering, Proximity relation, Local energy, Guide point, Face clustering BibRef

Leopold, N.[Nadiia], Rose, O.[Oliver],
UNIC: A fast nonparametric clustering,
PR(100), 2020, pp. 107117.
Elsevier DOI 2005
Cluster analysis, Hard (conventional, crisp) clustering, Nonparametric algorithms, Data mining, Big data BibRef

Dhariwal, S.[Sumit], Palaniappan, S.[Sellappan],
Image Normalization and Weighted Classification Using an Efficient Approach for SVM Classifiers,
IJIG(20), No. 4, October 2020, pp. 2050035.
DOI Link 2011
BibRef

Laloë, T.[Thomas],
Quantization based clustering: An iterative approach,
PRL(142), 2021, pp. 51-57.
Elsevier DOI 2101
Quantization, Clustering, Manhattan distance Based on Alter algorithm. BibRef

Gong, C.Y.[Chao-Yu], Su, Z.G.[Zhi-Gang], Wang, P.H.[Pei-Hong], Wang, Q.[Qian],
An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods,
PR(113), 2021, pp. 107751.
Elsevier DOI 2103
Evidential clustering, Belief-peaks, Disjoint neighborhood, Proximity data BibRef

Rosenfeld, J.[Jean], de Smet, Y.[Yves], Debeir, O.[Olivier], Decaestecker, C.[Christine],
Assessing partially ordered clustering in a multicriteria comparative context,
PR(114), 2021, pp. 107850.
Elsevier DOI 2103
Clustering for data characterized by peculiar quantitative features: i.e. large or small. Clustering, -means, Multicriteria, Partial ordering, Partition, Preference, Quality assessment BibRef

Liang, Z.[Zhou], Chen, P.[Pei],
An automatic clustering algorithm based on the density-peak framework and Chameleon method,
PRL(150), 2021, pp. 40-48.
Elsevier DOI 2109
DPC method, Cluster stability, Automatic clustering BibRef

Andrade, D.[Daniel], Fukumizu, K.[Kenji], Okajima, Y.[Yuzuru],
Convex covariate clustering for classification,
PRL(151), 2021, pp. 193-199.
Elsevier DOI 2110
Alternating direction method of multipliers, ADMM, Convex optimization, Model selection, Marginal likelihood, Text classification BibRef

Strobl, E.V.[Eric V.],
Automated hyperparameter selection for the PC algorithm,
PRL(151), 2021, pp. 288-293.
Elsevier DOI 2110
Infers causal relations using conditional independence. Causal discovery, Hyperparameter, PC Algorithm BibRef

Kurama, O.[Onesfole],
A new similarity-based classifier with Dombi aggregative operators,
PRL(151), 2021, pp. 229-235.
Elsevier DOI 2110
Classification, Similarity classifier, Dombi operator, OWA operator, Generalized mean BibRef

Hattori, T.[Takayuki], Inoue, K.[Kohei], Hara, K.[Kenji],
Rolling Guidance Filter as a Clustering Algorithm,
IEICE(E104-D), No. 10, October 2021, pp. 1576-1579.
WWW Link. 2110
BibRef

Zhao, S.P.[Shu-Ping], Wu, J.G.[Ji-Gang], Zhang, B.[Bob], Fei, L.[Lunke],
Low-rank inter-class sparsity based semi-flexible target least squares regression for feature representation,
PR(123), 2022, pp. 108346.
Elsevier DOI 2112
Least squares regression, Low-rank inter-class sparsity, Feature representation, Image classification BibRef

Ma, W.C.[Wen-Chi], Tu, X.M.[Xue-Min], Luo, B.[Bo], Wang, G.H.[Guang-Hui],
Semantic clustering based deduction learning for image recognition and classification,
PR(124), 2022, pp. 108440.
Elsevier DOI 2203
Deduction learning, Clustering prior, Semantic space, Smooth semantic clustering BibRef

Kadioglu, B.[Berkan], Tian, P.[Peng], Dy, J.[Jennifer], Erdogmus, D.[Deniz], Ioannidis, S.[Stratis],
Sample complexity of rank regression using pairwise comparisons,
PR(130), 2022, pp. 108688.
Elsevier DOI 2206
Bradley-Terry and Thurstone. Sample complexity, Rank regression, Pairwise comparisons, Features BibRef

Zhao, Q.C.[Qing-Chao], Li, L.[Long], Chu, Y.[Yan], Yang, Z.[Zhen], Wang, Z.K.[Zheng-Kui], Shan, W.[Wen],
Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Hou, J.[Jian], Yuan, H.Q.[Hua-Qiang], Pelillo, M.[Marcello],
Towards Parameter-Free Clustering for Real-World Data,
PR(134), 2023, pp. 109062.
Elsevier DOI 2212
Clustering, Real-world data, Dominant set, Density peak BibRef

Niu, C.[Chuang], Shan, H.M.[Hong-Ming], Wang, G.[Ge],
SPICE: Semantic Pseudo-Labeling for Image Clustering,
IP(31), 2022, pp. 7264-7278.
IEEE DOI 2212

WWW Link. Improve intra-class similarity and inter-class difference. Semantics, Training, Head, Clustering algorithms, SPICE, Clustering methods, Prototypes, Deep clustering, representation learning BibRef

Ding, S.F.[Shi-Fei], Li, C.[Chao], Xu, X.[Xiao], Ding, L.[Ling], Zhang, J.[Jian], Guo, L.[Lili], Shi, T.H.[Tian-Hao],
A Sampling-Based Density Peaks Clustering Algorithm for Large-Scale Data,
PR(136), 2023, pp. 109238.
Elsevier DOI 2301
Density peaks clustering, Sampling method, TI search strategy, Large-scale data BibRef

del Aguila-Pla, P.[Pol], Boquet-Pujadas, A.[Aleix], Jaldén, J.[Joakim],
Convex Quantization Preserves Logconcavity,
SPLetters(29), 2022, pp. 2697-2701.
IEEE DOI 2301
Quantization (signal), Data models, Detectors, Biological system modeling, Programmable logic arrays, inverse problems BibRef

Acampora, G.[Giovanni], Chiatto, A.[Angela], Vitiello, A.[Autilia],
Training circuit-based quantum classifiers through memetic algorithms,
PRL(170), 2023, pp. 32-38.
Elsevier DOI 2306
Quantum machine learning, Variational quantum circuits, Quantum classifiers, Memetic algorithms, Optimization BibRef

Ye, H.J.[Han-Jia], Zhou, D.W.[Da-Wei], Hong, L.[Lanqing], Li, Z.G.[Zhen-Guo], Wei, X.S.[Xiu-Shen], Zhan, D.C.[De-Chuan],
Contextualizing Meta-Learning via Learning to Decompose,
PAMI(46), No. 1, January 2024, pp. 117-133.
IEEE DOI 2312
Encoding a learning strategy. Attribute discovery, contextualized model, few-shot learning, meta representation, meta-learning. BibRef

Davashi, R.[Razieh],
IME: Efficient list-based method for incremental mining of maximal erasable patterns,
PR(148), 2024, pp. 110166.
Elsevier DOI 2402
Erasable pattern mining, Maximal erasable patterns, Incremental mining, Dynamic data BibRef

Nie, F.P.[Fei-Ping], Xue, J.J.[Jing-Jing], Yu, W.Z.[Wei-Zhong], Li, X.L.[Xue-Long],
Fast Clustering With Anchor Guidance,
PAMI(46), No. 4, April 2024, pp. 1898-1912.
IEEE DOI 2403
Bipartite graph, Clustering methods, Optimization methods, Costs, Data models, Convex functions, Tuning, Bipartite graph, trivial solution BibRef


Liu, B.X.[Bo-Xiao], Song, G.L.[Guang-Lu], Zhang, M.Y.[Man-Yuan], You, H.H.[Hai-Hang], Liu, Y.[Yu],
Switchable K-class Hyperplanes for Noise-Robust Representation Learning,
ICCV21(2999-3008)
IEEE DOI 2203

WWW Link. Representation learning, Training, Codes, Switches, Data models, Noise robustness, Faces, Recognition and classification, Representation learning BibRef

Mehrmohammadi, P.[Pooya], Hatami, M.[Mohammad], Moradi, P.[Parham],
A Graph-based Density Peaks Method by Employing Shortest Path for Data Clustering,
IPRIA21(1-8)
IEEE DOI 2201
Sensitivity, Image analysis, Shape, Clustering methods, Machine learning, Task analysis, Data clustering, Shortest path distance BibRef

Qian, R.[Rui], Meng, T.J.[Tian-Jian], Gong, B.Q.[Bo-Qing], Yang, M.H.[Ming-Hsuan], Wang, H.[Huisheng], Belongie, S.[Serge], Cui, Y.[Yin],
Spatiotemporal Contrastive Video Representation Learning,
CVPR21(6960-6970)
IEEE DOI 2111
Visualization, Codes, Semisupervised learning, Spatial databases, Spatiotemporal phenomena, Pattern recognition BibRef

Yuan, X.[Xin], Lin, Z.[Zhe], Kuen, J.[Jason], Zhang, J.M.[Jian-Ming], Wang, Y.[Yilin], Maire, M.[Michael], Kale, A.[Ajinkya], Faieta, B.[Baldo],
Multimodal Contrastive Training for Visual Representation Learning,
CVPR21(6991-7000)
IEEE DOI 2111
Training, Visualization, Image segmentation, Protocols, Scalability, Semantics, Object detection BibRef

Hu, W.[Wei], Zhao, Q.[QiHao], Huang, Y.[Yangyu], Zhang, F.[Fan],
P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions,
ICPR21(1882-1889)
IEEE DOI 2105
Training, Neural networks, Benchmark testing, Computational efficiency, Noise measurement, Task analysis BibRef

Davis, J.[Jim], Liang, T.[Tong], Enouen, J.[James], Ilin, R.[Roman],
Hierarchical Classification with Confidence using Generalized Logits,
ICPR21(1874-1881)
IEEE DOI 2105
Estimation, Probabilistic logic, Reliability BibRef

Astorga, N.[Nicolás], Huijse, P.[Pablo], Protopapas, P.[Pavlos], Estévez, P.[Pablo],
MPCC: Matching Priors and Conditionals for Clustering,
ECCV20(XXIII:658-677).
Springer DOI 2011
BibRef

Hadad, N.[Naama], Wolf, L.B.[Lior B.], Shahar, M.[Moni],
A Two-Step Disentanglement Method,
CVPR18(772-780)
IEEE DOI 1812
Training, Encoding, Task analysis, Image reconstruction, Image color analysis, Visualization Code:
WWW Link. BibRef

Zhang, Z.[Zheng], Liu, L.[Li], Qin, J.[Jie], Zhu, F.[Fan], Shen, F.M.[Fu-Min], Xu, Y.[Yong], Shao, L.[Ling], Shen, H.T.[Heng Tao],
Highly-Economized Multi-view Binary Compression for Scalable Image Clustering,
ECCV18(XII: 731-748).
Springer DOI 1810
BibRef

Hjouji, A., Jourhmane, M., El-Mekkaoui, J., Qjidaa, H., El Khalfi, A.,
Image clustering based on hermetian positive definite matrix and radial Jacobi moments,
ISCV18(1-6)
IEEE DOI 1807
Hermitian matrices, Jacobian matrices, Zernike polynomials, image processing, pattern clustering, Radial Jacobi moment BibRef

Adil, B.H., Youssef, G., Abderrahim, E.Q.,
HVS-MRMR wrapper method for variables selection,
ISCV17(1-4)
IEEE DOI 1710
multilayer perceptrons, pattern classification, classification problems, heuristic variable selection, multilayer perceptron, Classification algorithms, Heuristic Variable Selection HVS. Minimum Redundancy Maximum Relevance MRMR, classification, BibRef

Tasaki, H.[Hajime], Lenz, R.[Reiner], Chao, J.H.[Jin-Hui],
Simplex-based dimension estimation of topological manifolds,
ICPR16(3609-3614)
IEEE DOI 1705
Clustering algorithms, Estimation, Manifolds, Pattern recognition, Principal component analysis, Topology BibRef

Bandyopadhyay, S., Murty, M.N.,
Axioms to characterize efficient incremental clustering,
ICPR16(450-455)
IEEE DOI 1705
Algorithm design and analysis, Big Data, Clustering algorithms, Computational complexity, Machine learning algorithms, Merging, Partitioning, algorithms BibRef

Spampinato, G., Bruna, A.R., Curti, S., d'Alto, V.,
Advanced low cost clustering system,
IPTA16(1-5)
IEEE DOI 1703
cameras BibRef

Babaeian, A., Bayestehtashlc, A., Babaee, M., Bandarabadi, M., Ghadesi, A., Dourado, A.,
Angle constrained path for clustering of multiple manifolds,
ICIP15(4446-4450)
IEEE DOI 1512
BibRef

Slaoui, S.C., Lamari, Y.,
Clustering of large data based on the relational analysis,
ISCV15(1-7)
IEEE DOI 1506
integer programming BibRef

Zhang, Q.L.[Qi-Lin], Hua, G.[Gang], Liu, W.[Wei], Liu, Z.C.[Zi-Cheng], Zhang, Z.Y.[Zheng-You],
Can Visual Recognition Benefit from Auxiliary Information in Training?,
ACCV14(I: 65-80).
Springer DOI 1504
BibRef

Jayaraman, D.[Dinesh], Sha, F.[Fei], Grauman, K.[Kristen],
Decorrelating Semantic Visual Attributes by Resisting the Urge to Share,
CVPR14(1629-1636)
IEEE DOI 1409
attribute conflation Also use structure. BibRef

Feng, J.S.[Jia-Shi], Jegelka, S.[Stefanie], Yan, S.C.[Shui-Cheng], Darrell, T.J.[Trevor J.],
Learning Scalable Discriminative Dictionary with Sample Relatedness,
CVPR14(1645-1652)
IEEE DOI 1409
BibRef

Moraes, R.M.[Ronei M.], Machado, L.S.[Liliane S.], Prade, H.[Henri], Richard, G.[Gilles],
Supervised Classification Using Homogeneous Logical Proportions for Binary and Nominal Features,
CIARP13(I:165-173).
Springer DOI 1311
BibRef

Song, C.F.[Chun-Feng], Liu, F.[Feng], Huang, Y.Z.[Yong-Zhen], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Auto-encoder Based Data Clustering,
CIARP13(I:117-124).
Springer DOI 1311
BibRef

Bagdanov, A.D.[Andrew D.], del Bimbo, A.[Alberto], di Fina, D.[Dario], Karaman, S.[Svebor], Lisanti, G.[Giuseppe], Masi, I.[Iacopo],
Multi-target Data Association Using Sparse Reconstruction,
CIAP13(II:239-248).
Springer DOI 1309
Rely on multiple instances rather than average of them. BibRef

Hernŕndez-Leňn, R.[Raudel],
Improving the Accuracy of CAR-based Classifiers by Combining Netconf Measure and Dynamic -K Mechanism,
CIARP15(603-610).
Springer DOI 1511
BibRef
Earlier:
Dynamic K: A Novel Satisfaction Mechanism for CAR-Based Classifiers,
CIARP13(I:141-148).
Springer DOI 1311
BibRef

Hernández-León, R.[Raudel], Hernández-Palancar, J.[José], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
CAR-NF+: An Improved Version of CAR-NF Classifier,
CIARP12(455-462).
Springer DOI 1209
Class Association Rules BibRef

Pinilla-Buitrago, L.A.[Laura Alejandra], Martínez-Trinidad, J.F.[José Francisco],
New Penalty Scheme for Optimal Subsequence Bijection,
CIARP13(I:206-213).
Springer DOI 1311
BibRef

Cope, J.S.[James S.], Remagnino, P.[Paolo],
Classification of High-Dimension PDFs Using the Hungarian Algorithm,
SSSPR12(727-733).
Springer DOI 1211
BibRef
And:
Utilizing the Hungarian Algorithm for Improved Classification of High-Dimension Probability Density Functions in an Image Recognition Problem,
ACIVS12(268-277).
Springer DOI 1209
BibRef

Dubout, C.[Charles], Fleuret, F.[Francois],
Tasting families of features for image classification,
ICCV11(929-936).
IEEE DOI 1201
Different types of features. BibRef

Zdunek, R.[Rafal],
Uni-orthogonal Nonnegative Tucker Decomposition for Supervised Image Classification,
CIAP11(I: 88-97).
Springer DOI 1109
BibRef

Yáńez-Márquez, C.[Cornelio], López-Yáńez, I.[Itzamá], Sáenz Morales, G.D.[Guadalupe De_la_Luz],
Analysis and Prediction of Air Quality Data with the Gamma Classifier,
CIARP08(651-658).
Springer DOI 0809
BibRef

Lukin, V.V.[Vladimir V.], Ponomarenko, N.N.[Nikolay N.], Kurekin, A.A.[Andrey A.], Lever, K.[Kenneth], Pogrebnyak, O.[Oleksiy], Sanchez Fernandez, L.P.[Luis Pastor],
Approaches to Classification of Multichannel Images,
CIARP06(794-803).
Springer DOI 0611
BibRef

Awad, M., Wang, L.[Lei], Chin, Y.H.[Yu-Han], Khan, L., Chen, G., Chebil, F.,
A framework for image classification,
Southwest06(134-138).
IEEE DOI 0603
BibRef

Beder, C.[Christian],
Agglomerative Grouping of Observations by Bounding Entropy Variation,
DAGM05(101).
Springer DOI 0509
BibRef

Zhou, D.Y.[Deng-Yong], Schölkopf, B.[Bernhard],
Learning from Labeled and Unlabeled Data Using Random Walks,
DAGM04(237-244).
Springer DOI 0505
BibRef

Zhu, H.[Hui], Huang, J.H.[Jian-Hua], Tang, X.L.[Xiang-Long],
Comparing decision boundary curvature,
ICPR04(III: 450-453).
IEEE DOI 0409
BibRef

Tomiya, M.[Mitsuyoshi], Kikuchi, S.[Seitaro],
Application of Modified Counter-Propagation for Satellite Image Classification,
PCV02(B: 277). 0305
BibRef

Veeramachaneni, S., Fujisawa, H., Liu, C.L.[Cheng-Lin], Nagy, G.,
Style-conscious quadratic field classifier,
ICPR02(II: 72-75).
IEEE DOI 0211
BibRef

Aujol, J.F.[Jean-François], Aubert, G.[Gilles], Blanc-Féraud, L.[Laure],
Supervised classification for textured images,
INRIARR-4640, Novembre 2002.
HTML Version. 0306
BibRef

Aubert, G.[Gilles], Blanc-Feraud, L.[Laure], March, R.[Riccardo],
Gamma-convergence of discrete functionals with non convex perturbation for image classification,
INRIARR-4560, September 2002.
HTML Version. 0211
BibRef

Barata, T., Pina, P.,
Improving classification rates by modelling the clusters of trainings sets in features space using mathematical morphology operators,
ICPR02(II: 328-331).
IEEE DOI 0211
BibRef
And: ICPR02(IV: 90-93).
IEEE DOI 0211
BibRef

Carrilero, A.C., Maitre, H., Roux, M.,
Material determination from reflectance properties in aerial urban images,
CIAP01(553-558).
IEEE DOI 0210
BibRef

Elad, M.[Michael], Hel-Or, Y.[Yacov], Keshet, R.[Renato],
Pattern Detection Using a Maximal Rejection Classifier,
VF01(514 ff.).
Springer DOI 0209
BibRef

de Souza, K.M.A., Kent, J.T., Mardia, K.V.,
Estimation of Objects in Highly Variable Images Using Markov Chain Monte Carlo,
BMVC97(xx-yy).
HTML Version. 0209
BibRef

Hoque, S., Sirlantzis, K., Fairhurst, M.C.,
Bit plane decomposition and the scanning n-tuple classifier,
FHR02(207-211).
IEEE Top Reference. 0209
BibRef

Li, C.H.,
Constrained Minimum Cut for Classification Using Labeled and Unlabeled Data,
CVPR01(II:597-602).
IEEE DOI 0110
BibRef

Chen, H., Meer, P., Tyler, D.E.,
Robust Regression for Data with Multiple Structures,
CVPR01(I:1069-1075).
IEEE DOI 0110
To be able to cope with data containing multiple structures the techniques innate to vision (Hough and RANSAC) should be combined with the robust methods customary in statistics. BibRef

Duin, R.P.W.[Robert P.W.],
Classifiers in Almost Empty Spaces,
ICPR00(Vol II: 1-7).
IEEE DOI 0009
BibRef

Hartelius, K.,
A CART Extension using Quadratic Decision Borders,
SCIA99(Statistical Methods). BibRef 9900

Erenshteyn, R., Saxe, D., Laskov, P., Foulds, R.,
Distributed output encoding for multi-class pattern recognition,
CIAP99(229-234).
IEEE DOI 9909
BibRef

Abrantes, A.J., Marques, J.S.,
A Method for Dynamic Clustering of Data,
BMVC98(xx-yy). BibRef 9800

Simon, U.[Ute], Berndtgen, M.[Manfred],
WaveStat: Cluster Analysis of Image Data and Wavelet Coefficients,
ICPR98(Vol II: 1622-1625).
IEEE DOI 9808
BibRef

Shah, S.[Shishir], Aggarwal, J.,
A Hybrid Architecture for Performance Reasoning in Classification Systems,
ICPR98(Vol I: 326-330).
IEEE DOI 9808
BibRef

Le Bourgeois, F.[Frank], Frelicot, C.[Carl],
A Pretopology-Based Supervised Pattern Classifier,
ICPR98(Vol I: 106-109).
IEEE DOI 9808
BibRef

Le Bourgeois, F.[Frank], Emptoz, H.[Hubert],
Pretopological approach for supervised learning,
ICPR96(IV: 256-260).
IEEE DOI 9608
(I.N.S.A. de Lyon, F) BibRef

Ho, T., Kleinberg, E.,
Building Projectable Classifiers of Arbitrary Complexity,
ICPR96(II: 880-885).
IEEE DOI 9608
(ATT Bell Laboratories, USA) BibRef

Sinclair, D.,
Cluster Based Texture Analysis,
ICPR96(II: 825-829).
IEEE DOI 9608
(Technical Univ. Graz, A) BibRef

Chang, I., Loew, M.,
Classification with Nonexclusive Patterns,
ICPR96(II: 116-120).
IEEE DOI 9608
(The George Washington Univ., USA) BibRef

Chang, I., Loew, M.,
Pattern recognition with new class discovery,
CVPR91(438-443).
IEEE DOI 0403
BibRef

Sipper, M., Yeshurun, Y.,
Pattern classification using teurons,
ICPR90(I: 433-437).
IEEE DOI 9006
BibRef

Hilbert, E.E.[Edward E.],
Cluster Compression Algorithm, A Joint Clustering Data Compression Concept,
JPLPublication 77-43, December 1, 1977. (NASA, JPL, CIT, Pasadena, CA 91103). Cluster compression algorithm - Landsat application area: describe by cluster, represent points coded from cluster; removal of redundant data method suited to computer interpretation. BibRef 7712

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


Last update:Mar 16, 2024 at 20:36:19