14.2.8 Detecting Clusters and Number of Clusters

Pospisil, A.,
AMOS: the 'learning' multiclass pattern classifier,
PR(3), No. 3, October 1971, pp. 253-268.
WWW Version. 0309
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Dubes, R.C.[Richard C.],
How many clusters are best? - An experiment,
PR(20), No. 6, 1987, pp. 645-663.
WWW Version. 0309
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Begovich, C.L., Kane, V.E.,
Estimating the number of groups and group membership using simulation cluster analysis,
PR(15), No. 4, 1982, pp. 335-342.
WWW Version. 0309
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Soklic, M.E.[Milan E.],
Adaptive model for decision making,
PR(15), No. 6, 1982, pp. 485-493.
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Soklic, M.E.[Milan E.],
The partition of N-dimensional space, using shells,
PR(17), No. 3, 1984, pp. 339-344.
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di Gesù, V., Sacco, B.,
Some statistical properties of the minimum spanning forest,
PR(16), No. 5, 1983, pp. 525-531.
WWW Version. 0309
Finds cluster patterns in a random graph of points. BibRef

Dubes, R.C.[Richard C.], Hoffman, R.L.[Richard L.],
Remarks on some statistical properties of the minimum spanning forest,
PR(19), No. 1, 1986, pp. 49-53.
WWW Version. 0309
BibRef

Suen, C.Y., Wang, Q.R.,
ISOETRP: An interactive clustering algorithm with new objectives,
PR(17), No. 2, 1984, pp. 211-219.
WWW Version. 0309
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Zhang, Q.W.[Qi-Wen], Wang, Q.R.[Qing Ren],
Knowledge based ISOETRP clustering procedure,
ICPR88(II: 1233-1235).
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BibRef

Jolion, J.M.[Jean-Michel], Rosenfeld, A.[Azriel],
Cluster Detection in Background Noise,
PR(22), No. 5, 1989, pp. 603-607.
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BibRef

Sher, C.A., Rosenfeld, A.,
Pyramid Cluster Detection and Delineation by Consensus,
PRL(12), 1991, pp. 477-482. BibRef 9100

de Biase, G.A., di Gesu, V., Sacco, B.,
Detection of Diffuse Clusters in Noise Background,
PRL(4), 1986, pp. 39-44. BibRef 8600

Kurita, T.[Takio],
An efficient agglomerative clustering algorithm using a heap,
PR(24), No. 3, 1991, pp. 205-209.
WWW Version. 0401
BibRef
And:
Author's reply to comments,
PR(26), No. 7, July 1993, pp. 1121.
WWW Version. 0401
BibRef

Cho, T.H.[Tai-Hoon],
Comments on 'An efficient agglomerative clustering algorithm using a heap',
PR(26), No. 7, July 1993, pp. 1121.
WWW Version. 0401
BibRef

Krishnapuram, R., Freg, C.P.,
Fitting An Unknown Number of Lines and Planes to Image Data Through Compatible Cluster Merging,
PR(25), No. 4, April 1992, pp. 385-400.
WWW Version. BibRef 9204

Aldaoud, M.B., Roberts, S.A.,
New Methods for the Initialization of Clusters,
PRL(17), No. 5, May 1 1996, pp. 451-455. 9606
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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,
PRL(17), No. 11, September 16 1996, pp. 1141-1150. 9611
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Herbin, M., Bonnet, N., Vautrot, P.,
Estimation of the number of clusters and influence zones,
PRL(22), No. 14, December 2001, pp. 1557-1568.
Elsevier DOI Link 0110
BibRef

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 Abstract.
WWW Version. 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 Abstract.
WWW Version. BibRef

Frigui, H.[Hichem], Krishnapuram, R.[Raghu],
Clustering by Competitive Agglomeration,
PR(30), No. 7, July 1997, pp. 1109-1119.
WWW Version. 9707
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Frigui, H.[Hichem],
Clustering: Algorithms and Applications,
IPTA08(1-11).
IEEE DOI Link 0811
BibRef

Frigui, H.[Hichem],
Membershipmap: data transformation based on membership aggregation,
ICPR04(II: 463-466).
IEEE DOI Link 0409
BibRef

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.
WWW Version. 0707
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

Sbai, E.,
Cluster analysis by adaptive rank-order filters,
PR(34), No. 10, October 2001, pp. 2015-2027.
WWW Version. 0108
BibRef

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

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.
WWW Version. 0304
BibRef

Huband, J.M.[Jacalyn M.], Bezdek, J.C.[James C.], Hathaway, R.J.[Richard J.],
bigVAT: Visual assessment of cluster tendency for large data sets,
PR(38), No. 11, November 2005, pp. 1875-1886.
WWW Version. 0509
BibRef

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 Version. Clustering; Similarity measures; Cluster validity; Data visualization; Scalability 0606
BibRef

Hathaway, R.J.[Richard J.], Bezdek, J.C.[James C.], Huband, J.M.[Jacalyn M.],
Maximin Initialization for Cluster Analysis,
CIARP06(14-26).
Springer DOI Link 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 Version. 0307
BibRef

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 Version. 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.
WWW Version. 0409
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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 Link 0508
BibRef

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.
WWW Version. 0604
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 Version. 0611
Clustering; Gaussian mixture model; Single-pass; Large data sets BibRef

Karkkainen, I., Franti, P.,
Dynamic local search for clustering with unknown number of clusters,
ICPR02(II: 240-243).
IEEE DOI Link 0211
BibRef

Nagai, A.[Ayumu],
Inappropriateness of the criterion of k-way normalized cuts for deciding the number of clusters,
PRL(28), No. 15, 1 November 2007, pp. 1981-1986.
WWW Version. 0711
Spectral clustering; Number of clusters; Cluster validation 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.
WWW Version. 0711
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 Link 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 Link 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 Link 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 Link 1011
BibRef

Lin, Y.Y.[Yen-Yu], Liu, T.L.[Tyng-Luh], Fuh, C.S.[Chiou-Shann],
Multiple Kernel Learning for Dimensionality Reduction,
PAMI(33), No. 6, June 2011, pp. 1147-1160.
IEEE DOI Link 1105
Multiple descriptors. Transform into unified space. 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 Link
WWW Version. 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.
WWW Version. BibRef 0600

Wu, M., and Schölkopf, B.,
A Local Learning Approach for Clustering,
NIPS06(1529-1536).
WWW Version. BibRef 0600

Dagher, I., Dahdah, K.,
Adaptive bandwidth mode detection algorithm,
IET-IPR(5), No. 8, 2011, pp. 645-660.
WWW Version. 1108
recover the correct density function. BibRef

Liu, Q.[Qiegen], Wang, S.S.[Shan-Shan], Luo, J.H.[Jian-Hua],
A novel predual dictionary learning algorithm,
JVCIR(23), No. 1, January 2012, pp. 182-193.
Elsevier DOI Link
WWW Version. 1112
Dictionary learning; Sparse representation; Predual proximal point algorithm; Bregman iteration method; Iterated refinement property; Gradient descent; Majorization-minimization; Image denoising BibRef

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

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 Link 1106
Mutual Information BibRef

Capitaine, H.L.[Hoel Le], Frelicot, C.[Carl],
On Selecting an Optimal Number of Clusters for Color Image Segmentation,
ICPR10(3388-3391).
IEEE DOI Link 1008
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 Link 0909
Segmentation. Number of clusters. See also Multistage Branch-and-Bound Merging for Planar Surface Segmentation in Disparity Space. BibRef

Zhang, X.[Xiao], Liang, L.[Lin], Shum, H.Y.[Heung-Yeung],
Spectral error correcting output codes for efficient multiclass recognition,
ICCV09(1111-1118).
IEEE DOI Link 0909
ECOC framework extends to any binary classifier in multi-class case. NP Hard problem. Get approximation. BibRef

Yang, J.J.[Jing-Jing], Li, Y.N.[Yuan-Ning], Tian, Y.H.[Yong-Hong], Duan, L.Y.[Ling-Yu], Gao, W.[Wen],
Group-sensitive multiple kernel learning for object categorization,
ICCV09(436-443).
IEEE DOI Link 0909
BibRef

Zhang, R.G.[Rong-Guo], Wang, C.H.[Chun-Heng], Xiao, B.H.[Bai-Hua],
A strategy of classification via sparse dictionary learned by non-negative K-SVD,
Subspace09(117-122).
IEEE DOI Link 0910
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 Link 0812
BibRef

Li, F.J.[Fa-Jie], Klette, R.[Reinhard],
Recovery Rate of Clustering Algorithms,
PSIVT09(1058-1069).
Springer DOI Link 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 Link 0812
BibRef

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

Blaschko, M.B.[Matthew B.], Lampert, C.H.[Christoph H.],
Correlational spectral clustering,
CVPR08(1-8).
IEEE DOI Link 0806
BibRef

Lampert, C.H.[Christoph H.], Blaschko, M.B.[Matthew B.],
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection,
DAGM08(xx-yy).
Springer DOI Link 0806
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 Link 0709
Discover discriminable features for classification. BibRef

Grim, J.[Jirí],
EM Cluster Analysis for Categorical Data,
SSPR06(640-648).
Springer DOI Link 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 Link 0608
A single cluster, not multiple clusters. BibRef

Yan, S.C.[Shui-Cheng], Yuan, T.Q.A.[Tian-Qi-Ang], Tang, X.[Xiaoou],
Learning Semantic Patterns with Discriminant Localized Binary Projections,
CVPR06(I: 168-174).
IEEE DOI Link 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 Link 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 Link 0505
BibRef

Ke, Q.[Qifa], Kanade, T.,
Robust subspace clustering by combined use of kNND metric and SVD algorithm,
CVPR04(II: 592-599).
IEEE Abstract. 0408
Kth-Nearest-Neighbor, finds the clusters. BibRef

Law, M.H.C.[Martin H.C.], Topchy, A.P.[Alexander P.], Jain, A.K.,
Multiobjective data clustering,
CVPR04(II: 424-430).
IEEE Abstract. 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 Abstract. 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 Link BibRef 9600

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

Bandapadhay, A., Fu, J.L.,
Searching parameter spaces with noisy linear constraints,
CVPR88(550-555).
IEEE Abstract. 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
Nearest Neighbor Classification .