14.2.9 Detecting Clusters and Number of Clusters, Number of Classes

Chapter Contents (Back)
Number of Clusters. Clusters Detection. Clustering. 9905

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Kurita, T.[Takio],
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Cho, T.H.[Tai-Hoon],
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Krishnapuram, R.[Raghu], Freg, C.P.[Chih-Pin],
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Aldaoud, M.B., Roberts, S.A.,
New Methods for the Initialization of Clusters,
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Herbin, M., Bonnet, N., Vautrot, P.,
A Clustering Method Based on the Estimation of the Probability Density-Function and on the Skeleton by Influence Zones: Application to Image-Processing,
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Herbin, M., Bonnet, N., Vautrot, P.,
Estimation of the number of clusters and influence zones,
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Frigui, H.[Hichem], Krishnapuram, R.,
A Robust Competitive Clustering Algorithm with Applications in Computer Vision,
PAMI(21), No. 5, May 1999, pp. 450-465.
IEEE DOI Find the right number of clusters, starting with a lot of clusters. BibRef 9905

Frigui, H.[Hichem], Krishnapuram, R.[Raghu],
A Robust Algorithm for Automatic Extraction of an Unknown Number of Clusters from Noisy Data,
PRL(17), No. 12, October 25 1996, pp. 1223-1232. 9612
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Earlier:
A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers,
CVPR96(550-555).
IEEE DOI BibRef

Frigui, H.[Hichem], Krishnapuram, R.[Raghu],
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Frigui, H.[Hichem],
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Frigui, H.[Hichem],
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ICPR04(II: 463-466).
IEEE DOI 0409
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Frigui, H.[Hichem], Hwang, C.[Cheul], Rhee, F.C.H.[Frank Chung-Hoon],
Clustering and aggregation of relational data with applications to image database categorization,
PR(40), No. 11, November 2007, pp. 3053-3068.
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Relational clustering; Feature aggregation; Image database categorization BibRef

Nakamura, E.[Eiji], Kehtarnavaz, N.[Nasser],
Determining number of clusters and prototype locations via multi-scale clustering,
PRL(19), No. 14, December 1998, pp. 1265-1283. BibRef 9812

Kothari, R.[Ravi], Pitts, D.[Dax],
On finding the number of clusters,
PRL(20), No. 4, April 1999, pp. 405-416. BibRef 9904

Pan, W.[Wei],
Shrinking classification trees for boot-strap aggregation,
PRL(20), No. 8, August 1999, pp. 961-965. BibRef 9908

Tibshirani, R., Guenther, W., Hastie, T.,
Estimating the number of clusters in a data set via the gap statistic,
RoyalStat(B 63), 2001, pp. 411-423.
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Sbai, E.,
Cluster analysis by adaptive rank-order filters,
PR(34), No. 10, October 2001, pp. 2015-2027.
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Veenman, C.J.[Cor J.], Reinders, M.J.T.[Marcel J.T.], Backer, E.[Eric],
A Maximum Variance Cluster Algorithm,
PAMI(24), No. 9, September 2002, pp. 1273-1280.
IEEE Abstract. 0209
minimize the sum-of-squared-error with a constraint on cluster variance. BibRef

Sugar, C.A., James, G.M.,
Finding the number of clusters in a data set: An information theoretic approach,
ASAJ(98), 2003, pp. 750-763.
DOI Link 1706
BibRef

Hathaway, R.J.[Richard J.], Bezdek, J.C.[James C.],
Visual cluster validity for prototype generator clustering models,
PRL(24), No. 9-10, June 2003, pp. 1563-1569.
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Huband, J.M.[Jacalyn M.], Bezdek, J.C.[James C.], Hathaway, R.J.[Richard J.],
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Hathaway, R.J.[Richard J.], Bezdek, J.C.[James C.], Huband, J.M.[Jacalyn M.],
Scalable visual assessment of cluster tendency for large data sets,
PR(39), No. 7, July 2006, pp. 1315-1324.
WWW Link. Clustering; Similarity measures; Cluster validity; Data visualization; Scalability 0606
BibRef
Earlier:
Maximin Initialization for Cluster Analysis,
CIARP06(14-26).
Springer DOI 0611
BibRef

Franc, V.[Vojtech], Hlavác, V.[Václav],
An iterative algorithm learning the maximal margin classifier,
PR(36), No. 9, September 2003, pp. 1985-1996.
WWW Link. 0307
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And:
Greedy Algorithm for a Training Set Reduction in the Kernel Methods,
CAIP03(426-433).
Springer DOI 0311
BibRef
Earlier:
Multi-class support vector machine,
ICPR02(II: 236-239).
IEEE DOI 0211
BibRef

Uricár, M.[Michal], Franc, V.[Vojtech], Hlavác, V.[Václav],
Bundle Methods for Structured Output Learning: Back to the Roots,
SCIA13(162-171).
Springer DOI 1311
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Kim, D.W.[Dae-Won], Lee, K.H.[Kwang H.], Lee, D.[Doheon],
On cluster validity index for estimation of the optimal number of fuzzy clusters,
PR(37), No. 10, October 2004, pp. 2009-2025.
WWW Link. 0409
BibRef

Sun, H.J.[Hao-Jun], Wang, S.R.[Sheng-Rui], Jiang, Q.S.[Qing-Shan],
FCM-Based Model Selection Algorithms for Determining the Number of Clusters,
PR(37), No. 10, October 2004, pp. 2027-2037.
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Chen, S., Hong, X., Harris, C.J.,
Sparse Kernel Density Construction Using Orthogonal Forward Regression With Leave-One-Out Test Score and Local Regularization,
SMC-B(34), No. 4, August 2004, pp. 1708-1717.
IEEE Abstract. 0409
Alternative to SVM. BibRef

Tran, T.N., Wehrens, R., Hoekman, D.H., Buydens, L.M.C.,
Initialization of Markov random field clustering of large remote sensing images,
GeoRS(43), No. 8, August 2005, pp. 1912-1919.
IEEE DOI 0508
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Silva, H.B.[Helena Brás], Brito, P.[Paula], Pinto da Costa, J.[Joaquim],
A partitional clustering algorithm validated by a clustering tendency index based on graph theory,
PR(39), No. 5, May 2006, pp. 776-788.
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Unsupervised learning; Clustering algorithms; Clustering validity BibRef

Kärkkäinen, I.[Ismo], Fränti, P.[Pasi],
Gradual model generator for single-pass clustering,
PR(40), No. 3, March 2007, pp. 784-795.
WWW Link. 0611
BibRef
And:
Dynamic local search for clustering with unknown number of clusters,
ICPR02(II: 240-243).
IEEE DOI 0211
Clustering; Gaussian mixture model; Single-pass; Large data sets BibRef

Moussa, A.[Ahmed], Sbihi, A.[Abderrahmane], Postaire, J.G.[Jack-Gerard],
A Markov random field model for mode detection in cluster analysis,
PRL(29), No. 9, 1 July 2008, pp. 1197-1207.
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Markov field; Gibbs distribution; Potential function; Mode detection; Classification BibRef

Raykar, V.C.[Vikas C.], Duraiswami, R.[Ramani], Krishnapuram, B.[Balaji],
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets,
PAMI(30), No. 7, July 2008, pp. 1158-1170.
IEEE DOI 0806
maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data BibRef

Srinivasan, B.V.[Balaji Vasan], Duraiswami, R.[Ramani],
Efficient subset selection via the kernelized Rényi distance,
ICCV09(1081-1088).
IEEE DOI 0909
BibRef

Hochbaum, D.S.[Dorit S.],
Polynomial Time Algorithms for Ratio Regions and a Variant of Normalized Cut,
PAMI(32), No. 5, May 2010, pp. 889-898.
IEEE DOI 1003
For clustering group similar objects, each group is dissimilar for others. BibRef

He, Z.S.[Zhao-Shui], Cichocki, A.[Andrzej], Xie, S.L.[Sheng-Li], Choi, K.[Kyuwan],
Detecting the Number of Clusters in n-Way Probabilistic Clustering,
PAMI(32), No. 11, November 2010, pp. 2006-2021.
IEEE DOI 1011
BibRef

Tan, S.C.[Swee Chuan], Ting, K.M.[Kai Ming], Teng, S.W.[Shyh Wei],
A general stochastic clustering method for automatic cluster discovery,
PR(44), No. 10-11, October-November 2011, pp. 2786-2799.
Elsevier DOI 1101
Clustering; Ant-based clustering; Automatic cluster detection BibRef

Wu, M., Schölkopf, B., and Bakir, G.,
A Direct Method for Building Sparse Kernel Learning Algorithms,
MachLearnRes(7), No. 4, 2006, pp. 603-624.
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Wu, M., and Schölkopf, B.,
A Local Learning Approach for Clustering,
NIPS06(1529-1536).
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Dagher, I., Dahdah, K.,
Adaptive bandwidth mode detection algorithm,
IET-IPR(5), No. 8, 2011, pp. 645-660.
DOI Link 1108
recover the correct density function. BibRef

Qian, Q.A.[Qi-Ang], Chen, S.C.[Song-Can], Cai, W.L.[Wei-Ling],
Simultaneous clustering and classification over cluster structure representation,
PR(45), No. 6, June 2012, pp. 2227-2236.
Elsevier DOI 1202
Structure in data; Clustering learning; Classification learning; Simultaneous classification and clustering learning BibRef

Koonsanit, K.[Kitti], Jaruskulchai, C.[Chuleerat],
Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data,
IEICE(E95-D), No. 5, May 2012, pp. 1256-1263.
WWW Link. 1202
BibRef

Hansen, P.[Pierre], Ruiz, M.[Manuel], Aloise, D.[Daniel],
A VNS heuristic for escaping local extrema entrapment in normalized cut clustering,
PR(45), No. 12, December 2012, pp. 4337-4345.
Elsevier DOI 1208
Normalized cut; Clustering; Variable neighborhood search; Heuristics BibRef

Cheung, Y.M.[Yiu-Ming], Jia, H.[Hong],
Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number,
PR(46), No. 8, August 2013, pp. 2228-2238.
Elsevier DOI 1304
Clustering; Similarity metric; Categorical attribute; Numerical attribute; Number of clusters BibRef

Liu, C.[Cong], Zhou, A.[Aimin], Zhang, G.[Guixu],
Automatic clustering method based on evolutionary optimisation,
IET-CV(7), No. 4, 2013, pp. 258-271.
DOI Link 1307
Set the number of clusters. BibRef

Liu, C.[Cong], Zhou, A.[Aimin], Zhang, Q., Zhang, G.[Guixu],
Adaptive image segmentation by using mean-shift and evolutionary optimisation,
IET-IPR(8), No. 6, June 2014, pp. 327-333.
DOI Link 1407
BibRef

Long, D.[Di], Singh, V.P.,
An Entropy-Based Multispectral Image Classification Algorithm,
GeoRS(51), No. 12, 2013, pp. 5225-5238.
IEEE DOI 1312
entropy BibRef

Chi, Y.J.[Yue-Jie], Porikli, F.M.[Fatih M.],
Classification and Boosting with Multiple Collaborative Representations,
PAMI(36), No. 8, August 2014, pp. 1519-1531.
IEEE DOI 1407
BibRef
Earlier:
Connecting the dots in multi-class classification: From nearest subspace to collaborative representation,
CVPR12(3602-3609).
IEEE DOI 1208
Biomedical measurement BibRef

Kolesnikov, A.[Alexander], Trichina, E.[Elena], Kauranne, T.[Tuomo],
Estimating the number of clusters in a numerical data set via quantization error modeling,
PR(48), No. 3, 2015, pp. 941-952.
Elsevier DOI 1412
Clustering BibRef

Hennig, C.[Christian],
What are the true clusters?,
PRL(64), No. 1, 2015, pp. 53-62.
Elsevier DOI 1509
Constructivism BibRef

Fornells, A.[Albert], Rodrigo, Z.[Zaida], Rovira, X.[Xari], Sánchez, M.[Mónica], Santomà, R.[Ricard], Teixidó-Navarro, F.[Francesc], Golobardes, E.[Elisabet],
Promoting consensus in the concept mapping methodology: An application in the hospitality sector,
PRL(67, Part 1), No. 1, 2015, pp. 39-48.
Elsevier DOI 1511
Concept mapping methodology BibRef

Liang, Z.[Zhou], Chen, P.[Pei],
Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering,
PRL(73), No. 1, 2016, pp. 52-59.
Elsevier DOI 1604
Divide-and-conquer BibRef

Li, H.P.[Hua-Peng], Zhang, S.Q.[Shu-Qing], Ding, X.H.[Xiao-Hui], Zhang, C.[Ce], Dale, P.[Patricia],
Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets,
RS(8), No. 4, 2016, pp. 295.
DOI Link 1604
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Saki, F.[Fatemeh], Kehtarnavaz, N.[Nasser],
Online frame-based clustering with unknown number of clusters,
PR(57), No. 1, 2016, pp. 70-83.
Elsevier DOI 1605
Online clustering for streaming data BibRef

Luo, J.J.[Juan-Juan], Jiao, L.C.[Li-Cheng], Shang, R.[Ronghua], Liu, F.[Fang],
Learning simultaneous adaptive clustering and classification via MOEA,
PR(60), No. 1, 2016, pp. 37-50.
Elsevier DOI 1609
Multiobjective optimization BibRef

Zhu, Y.[Ye], Ting, K.M.[Kai Ming], Carman, M.J.[Mark J.],
Density-ratio based clustering for discovering clusters with varying densities,
PR(60), No. 1, 2016, pp. 983-997.
Elsevier DOI 1609
Density-ratio BibRef

Carton, C.[Cérès], Lemaitre, A.[Aurélie], Coüasnon, B.[Bertrand],
Eyes Wide Open: An interactive learning method for the design of rule-based systems,
IJDAR(20), No. 2, June 2017, pp. 91-103.
Springer DOI 1706
rule-based document recognition systems. User guided analysis of document corpus. BibRef

Zhang, H.[He], Patel, V.M.[Vishal M.],
Sparse Representation-Based Open Set Recognition,
PAMI(39), No. 8, August 2017, pp. 1690-1696.
IEEE DOI 1707
Not all classes presented during testing are known during training. Animals, Data models, Image reconstruction, Indexes, Pattern analysis, Testing, Training, Open set recognition, extreme value theory, sparse, representation-based, classification BibRef

Zhan, Q.M.[Qing-Ming], Deng, S.G.[Shu-Guang], Zheng, Z.H.[Zhi-Hua],
An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt,
IJGI(6), No. 9, 2017, pp. xx-yy.
DOI Link 1710
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Hayes, J.J.[James J.], Castillo, O.[Oscar],
A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size,
IJGI(6), No. 10, 2017, pp. xx-yy.
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Analysis of spatial distribution of organisms. BibRef


Liu, J.C.[Jun-Cheng], Lian, Z.H.[Zhou-Hui], Wang, Y.[Yi], Xiao, J.G.[Jian-Guo],
Incremental Kernel Null Space Discriminant Analysis for Novelty Detection,
CVPR17(4123-4131)
IEEE DOI 1711
Is the data really part of any current class. Algorithm design and analysis, Null space, Pattern recognition, Training, Training, data BibRef

Guo, P.C.[Peng-Cheng], Wang, X.[Xing], Wang, Y.B.[Yu-Bing], Cheng, Y.[Yue], Zhang, Y.[Ying],
Research on automatic determining clustering centers algorithm based on linear regression analysis,
ICIVC17(1016-1023)
IEEE DOI 1708
clustering center, density peak, linear regression, residual, analysis BibRef

Comiter, M.[Marcus], Cha, M.[Miriam], Kung, H.T., Teerapittayanon, S.[Surat],
Lambda means clustering: Automatic parameter search and distributed computing implementation,
ICPR16(2331-2337)
IEEE DOI 1705
Clustering algorithms, Computers, Distributed computing, Elbow, Measurement, Multicore processing, Partitioning, algorithms BibRef

Wang, Y., Li, Y., Zhao, Q.H.,
Coupling Regular Tessellation with RJMCMC Algorithm to Segment SAR Image With Unknown Number Of Classes,
ISPRS16(B7: 393-397).
DOI Link 1610
BibRef

Zhao, X.[Xuemei], Li, Y.[Yu], Zhao, Q.[Quanhua], Wang, C.Y.[Chun-Yan],
An Entropy-kl Strategy For Estimating Number Of Classes In Image Segmentation Issues,
ISPRS16(B7: 437-441).
DOI Link 1610
BibRef

Zhang, Z.M.[Zi-Ming], Chen, Y.T.[Yu-Ting], Saligrama, V.[Venkatesh],
Group Membership Prediction,
ICCV15(3916-3924)
IEEE DOI 1602
Predict whether a collection of instances share a certain semantic property. BibRef

Tepper, M.[Mariano], Sapiro, G.[Guillermo],
From Local to Global Communities in Large Networks Through Consensus,
CIARP15(659-666).
Springer DOI 1511
BibRef

Mohammed, A.J.[Athraa Jasim], Yusof, Y.[Yuhanis], Husni, H.[Husniza],
Determining Number of Clusters Using Firefly Algorithm with Cluster Merging for Text Clustering,
IVIC15(14-24).
Springer DOI 1511
BibRef

Kim, M.[Minkyu], Lim, J.M.[Jeong-Mook], Shin, H.[Heesook], Oh, C.[Changmok], Jeong, H.T.[Hyun-Tae],
Estimating the Number of Clusters with Database for Texture Segmentation Using Gabor Filter,
CVS15(435-444).
Springer DOI 1507
BibRef

Hautamäki, V.[Ville], Pöllänen, A.[Antti], Kinnunen, T.[Tomi], Lee, K.A.[Kong Aik], Li, H.Z.[Hai-Zhou], Fränti, P.[Pasi],
A Comparison of Categorical Attribute Data Clustering Methods,
SSSPR14(53-62).
Springer DOI 1408
BibRef

Kading, C.[Christoph], Freytag, A.[Alexander], Rodner, E.[Erik], Bodesheim, P.[Paul], Denzler, J.[Joachim],
Active learning and discovery of object categories in the presence of unnameable instances,
CVPR15(4343-4352)
IEEE DOI 1510
BibRef

Bodesheim, P.[Paul], Freytag, A.[Alexander], Rodner, E.[Erik], Denzler, J.[Joachim],
Local Novelty Detection in Multi-class Recognition Problems,
WACV15(813-820)
IEEE DOI 1503
Computational modeling BibRef

Bodesheim, P.[Paul], Freytag, A.[Alexander], Rodner, E.[Erik], Kemmler, M.[Michael], Denzler, J.[Joachim],
Kernel Null Space Methods for Novelty Detection,
CVPR13(3374-3381)
IEEE DOI 1309
kernel methods. Finding unknown objects. BibRef

Al-Rawi, M.S.[Mohammed Sadeq], Cunha, J.P.S.[João Paulo Silva],
Using Permutation Tests to Study How the Dimensionality, the Number of Classes, and the Number of Samples Affect Classification Analysis,
ICIAR12(I: 34-42).
Springer DOI 1206
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Kolesnikov, A.[Alexander], Trichina, E.[Elena],
Determining the Number of Clusters with Rate-Distortion Curve Modeling,
ICIAR12(I: 43-50).
Springer DOI 1206
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Mittal, M.[Mamta], Singh, V.P., Sharma, R.K.,
Random automatic detection of clusters,
ICIIP11(1-6).
IEEE DOI 1112
BibRef

Chen, G.L.[Guang-Liang], Maggioni, M.[Mauro],
Multiscale geometric and spectral analysis of plane arrangements,
CVPR11(2825-2832).
IEEE DOI 1106
Based on SVD clustering. BibRef

Gopalan, R.[Raghuraman], Sankaranarayanan, J.[Jagan],
Max-margin clustering: Detecting margins from projections of points on lines,
CVPR11(2769-2776).
IEEE DOI 1106
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Zeng, Z.M.[Zi-Ming], Wang, W.H.[Wen-Hui], Yang, L.Z.[Long-Zhi], Zwiggelaar, R.[Reyer],
Automatic Estimation of the Number of Segmentation Groups Based on MI,
IbPRIA11(532-539).
Springer DOI 1106
Mutual Information BibRef

Thakoor, N.[Ninad], Devarajan, V.[Venkat], Gao, J.X.[Jean X.],
Computation complexity of branch-and-bound model selection,
ICCV09(1895-1900).
IEEE DOI 0909
Segmentation. Number of clusters. See also Multistage Branch-and-Bound Merging for Planar Surface Segmentation in Disparity Space. BibRef

Hua, C.S.[Chun-Sheng], Sagawa, R.[Ryusuke], Yagi, Y.S.[Yasu-Shi],
Scale-invariant density-based clustering initialization algorithm and its application,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Li, F.J.[Fa-Jie], Klette, R.[Reinhard],
Recovery Rate of Clustering Algorithms,
PSIVT09(1058-1069).
Springer DOI 0901
Given old clusters, evaluation of performance to compute new clusters. See also Decomposing a Simple Polygon into Trapezoids. BibRef

Franti, P.[Pasi], Virmajoki, O.[Olli], Hautamaki, V.[Ville],
Probabilistic clustering by random swap algorithm,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhao, Q.P.[Qin-Pei], Hautamaki, V.[Ville], Fränti, P.[Pasi],
Knee Point Detection in BIC for Detecting the Number of Clusters,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Zhang, Z.M.[Zi-Ming], Chan, S.[Syin], Chia, L.T.[Liang-Tien],
Discriminative Signatures for Image Classification,
ICIP07(II: 197-200).
IEEE DOI 0709
Discover discriminable features for classification. BibRef

Grim, J.[Jirí],
EM Cluster Analysis for Categorical Data,
SSPR06(640-648).
Springer DOI 0608
Sequential estimation of components to guarantee a unique identification of clusters by means of EM algorithm. BibRef

Klawonn, F.[Frank],
Identifying Single Good Clusters in Data Sets,
IWICPAS06(160-167).
Springer DOI 0608
A single cluster, not multiple clusters. BibRef

Yan, S.C.[Shui-Cheng], Yuan, T.Q.[Tian-Qiang], Tang, X.[Xiaoou],
Learning Semantic Patterns with Discriminant Localized Binary Projections,
CVPR06(I: 168-174).
IEEE DOI 0606
Turn into a clustering problem. BibRef

Nasios, N.[Nikolaos], Bors, A.G.[Adrian G.],
Finding the Number of Clusters for Nonparametric Segmentation,
CAIP05(213).
Springer DOI 0509
BibRef

Zheng, X.[Xin], Lin, X.Y.[Xue-Yin],
Automatic determination of intrinsic cluster number family in spectral clustering using random walk on graph,
ICIP04(V: 3471-3474).
IEEE DOI 0505
BibRef

Law, M.H.C.[Martin H.C.], Topchy, A.P.[Alexander P.], Jain, A.K.,
Multiobjective data clustering,
CVPR04(II: 424-430).
IEEE DOI 0408
Cluster with multiple objective functions. Two stages, use all, integrate. BibRef

Zhang, H.[Hao], Malik, J.,
Learning a discriminative classifier using shape context distances,
CVPR03(I: 242-247).
IEEE DOI 0307
BibRef

Marazzi, A.[Andrea], Gamba, P., Mecocci, A., Semboloni, A.,
Automatic Selection of the Number of Clusters in Multidimensional Data Problems,
ICIP96(III: 631-634).
IEEE DOI BibRef 9600

Wallace, R.S., and Kanade, T.,
Finding Natural Clusters Having Minimal Description Lengths,
ICPR90(I: 438-442).
IEEE DOI BibRef 9000

Bandapadhay, A., Fu, J.L.,
Searching parameter spaces with noisy linear constraints,
CVPR88(550-555).
IEEE DOI 0403
predicated on some invariant properties of affine transformations and on the course-to-fine search paradigm. BibRef

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


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