14.2.2 Clustering, Classification, General

Chapter Contents (Back)
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,
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Jain, A.K., Murty, M.N., and Flynn, P.J.,
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Jain, A.K.[Anil K.],
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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.,
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Jain, A.K.,
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Jain, A.K., and Dubes, R.C.,
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Dubes, R.C.,
Cluster Analysis and Related Issues,
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Dubes, R.C.[Richard C.], Jain, A.K.[Anil K.],
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PR(11), No. 4, 1979, pp. 235-254.
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Bailey, Jr., T.A.[Thomas A.], Dubes, R.C.[Richard C.],
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PR(15), No. 2, 1982, pp. 61-83.
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Panayirci, E.[Erdal], Dubes, R.C.[Richard C.],
A test for multidimensional clustering tendency,
PR(16), No. 4, 1983, pp. 433-444.
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Earlier: Abstract: PR(16), No. 3, 1983, pp. Page 357.
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Kaminuma, T., Takekawa, T., Watanabe, S.,
Reduction of clustering problem to pattern recognition,
PR(1), No. 3, March 1969, pp. 195-205.
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Freeman, J.J.,
Experiments in discrimination and classification,
PR(1), No. 3, March 1969, pp. 207-218.
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Holdermann, F.,
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PR(3), No. 3, October 1971, pp. 243-251.
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Diday, E.[Edwin],
Optimization in non-hierarchical clustering,
PR(6), No. 1, June 1974, pp. 17-33.
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Biological profiles. Minerals. BibRef

Diday, E.[Edwin], Cucumel, G.,
Compatibility and consensus in numerical taxonomy,
ICPR88(II: 1059-1061).
IEEE DOI 8811
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Coray, G., Noetzel, A., Selkow, S.M.,
Order independence in local clustering algorithms,
CGIP(4), No. 2, June 1975, pp. 120-132.
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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.
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Kittler, J.V.,
A locally sensitive method for cluster analysis,
PR(8), No. 1, January 1976, pp. 23-33.
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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.
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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.
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Chittineni, C.B.,
Some approaches to optimal cluster labeling with applications to remote sensing,
PR(15), No. 3, 1982, pp. 201-216.
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Chittineni, C.B.,
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PR(12), No. 4, 1980, pp. 243-249.
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Massart, D.L.[Désiré L.], Plastria, F.[Frank], Kaufman, L.[Leonard],
Non-hierarchical clustering with MASLOC,
PR(16), No. 5, 1983, pp. 507-516.
WWW Link. 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.
WWW Link. 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.
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Dehne, F.,
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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.,
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PR(20), No. 5, 1987, pp. 547-568.
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Umesh, R.M.,
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PR(21), No. 4, 1988, pp. 393-400.
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Bryant, J.[Jack],
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PR(22), No. 1, 1989, pp. 45-48.
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Shekar, B., Murty, M.N.[M. Narasimha], Krishna, G.,
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PR(22), No. 1, 1989, pp. 65-74.
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Liu, S.T.[Song-Tyang], Tsai, W.H.[Wen-Hsiang],
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PR(22), No. 4, 1989, pp. 433-447.
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Postaire, J.G., Touzani, A.,
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PR(22), No. 5, 1989, pp. 477-489.
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Wilson, R., Spann, M.,
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PR(23), No. 12, 1990, pp. 1413-1425.
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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.
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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.
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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
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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
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Lakroum, S., Devlaminck, V., Terrier, P., Biela-Enberg, P., Postaire, J.G.,
Clustering of The Poincaré Vectors,
ICIP05(II: 1190-1193).
IEEE DOI 0512
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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
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Cubanski, D., Cyganski, D.,
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PAMI(17), No. 4, April 1995, pp. 403-417.
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Osbourn, G.C., Martinez, R.F.,
Empirically defined regions of influence for clustering analyses,
PR(28), No. 11, November 1995, pp. 1793-1806.
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Use human cluster judgments as the cluster criteria. BibRef

McLachlan, G.J., Krishnan, T.,
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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.
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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
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Lin, J.C.[Ja-Chen], Lin, W.J.[Wu-Ja],
Real-Time And Automatic 2-Class Clustering By Analytical Formulas,
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Lin, W.J.,
Two-class clustering of nonlinearly separable data by using shape-specific points,
IVMSP16(1-5)
IEEE DOI 1608
Clustering algorithms BibRef

Wharton, S.W.[Stephen W.],
A Generalized Histogram Clustering Scheme for Multidimensional Image Data,
PR(16), No. 2, 1983, pp. 193-199.
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Li, Q., Tufts, D.W.,
Principal Feature Classification,
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Murtagh, F.,
Contiguity-Constrained Clustering for Image Analysis,
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Doncarli, C., and Carpentier, E.L.,
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Boberg, J.[Jorma], Salakoski, T.[Tapio],
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Velthuizen, R.P.[Robert P.], Hall, L.O.[Lawrence O.], Clarke, L.P.[Laurence P.], Silbiger, M.L.[Martin L.],
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Ha, T.M.,
The Optimum Class-Selective Rejection Rule,
PAMI(19), No. 6, June 1997, pp. 608-615.
IEEE DOI 9708
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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.,
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IEEE DOI 9710
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Kastella, K.,
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Muzzolini, R.[Russell], Yang, Y.H.[Yee-Hong], Pierson, R.[Roger],
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Erol, H., Akdeniz, F.,
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Sukanya, P.[Phongsuphap], Takamatsu, R.[Ryo], Sato, M.[Makoto],
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IEEE DOI 9808
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Judd, D., McKinley, P.K., and Jain, A.K.,
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Chaudhuri, B.B., Bhowmik, P.R.,
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Chang, K.C., Yeh, M.F.,
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Blekas, K., Lagaris, I.E.,
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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
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Cazzanti, L.[Luca], Gupta, M.R.[Maya R.], Koppal, A.J.[Anjali J.],
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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],
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Clustering; Core grid; Grid size; Locality BibRef

Bounsiar, A.[Abdenour], Beauseroy, P.[Pierre], Grall-Maes, E.[Edith],
General solution and learning method for binary classification with performance constraints,
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Statistical hypothesis testing; Performance constraints; Neyman-Pearson criterion; Chow's rule; Classification with rejection option; Kernel methods 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.
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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.
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Include cost to be minimized, and decision options in classifier design. BibRef

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Supervised learning rule selection for multiclass decision with performance constraints,
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IEEE DOI 0812
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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
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Earlier:
GCA: A real-time grid-based clustering algorithm for large data set,
ICPR06(II: 740-743).
IEEE DOI 0609
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Earlier:
Mining Uncertain Data in Low-dimensional Subspace,
ICPR06(II: 748-751).
IEEE DOI 0609
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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,
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Submodular function optimization; Balanced clustering; Discrete optimization BibRef

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Computational method for the point cluster analysis on networks,
GeoInfo(15), No. 1, January 2011, pp. 167-189.
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Li, J.L.[Jun-Lin], Fu, H.G.[Hong-Guang],
Molecular dynamics-like data clustering approach,
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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,
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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.
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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
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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., Dy, J., Jordan, A.,
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

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PR(45), No. 4, 2012, pp. 1617-1626.
Elsevier DOI 1410
Bi-dimensional empirical mode decomposition (BEMD) BibRef

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Elsevier DOI 1606
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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
One Class Clustering, One Class Classification .


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