14.2.2 Clustering, Classification, General Methods

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-Núñez, 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

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