14.2.8 Detecting Clusters and Number of Clusters

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Clustering. 9905

Pospisil, A.,
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Finds cluster patterns in a random graph of points. BibRef

Dubes, R.C.[Richard C.], Hoffman, R.L.[Richard L.],
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Suen, C.Y., Wang, Q.R.,
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Zhang, Q.W.[Qi-Wen], Wang, Q.R.[Qing Ren],
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Jolion, J.M.[Jean-Michel], Rosenfeld, A.[Azriel],
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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.,
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Kurita, T.[Takio],
An efficient agglomerative clustering algorithm using a heap,
PR(24), No. 3, 1991, pp. 205-209.
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And:
Author's reply to comments,
PR(26), No. 7, July 1993, pp. 1121.
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Cho, T.H.[Tai-Hoon],
Comments on 'An efficient agglomerative clustering algorithm using a heap',
PR(26), No. 7, July 1993, pp. 1121.
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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.
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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.
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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.
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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,
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Earlier:
A Robust Clustering Algorithm Based on Competitive Agglomeration and Soft Rejection of Outliers,
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Frigui, H.[Hichem], Krishnapuram, R.[Raghu],
Clustering by Competitive Agglomeration,
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Frigui, H.[Hichem],
Clustering: Algorithms and Applications,
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Frigui, H.[Hichem],
Membershipmap: data transformation based on membership aggregation,
ICPR04(II: 463-466).
IEEE DOI Link 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.
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,
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Pan, W.[Wei],
Shrinking classification trees for boot-strap aggregation,
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Sbai, E.,
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WWW Version. 0108
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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.
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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,
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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,
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Hathaway, R.J.[Richard J.], Bezdek, J.C.[James C.], Huband, J.M.[Jacalyn M.],
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WWW Version. Clustering; Similarity measures; Cluster validity; Data visualization; Scalability 0606
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Hathaway, R.J.[Richard J.], Bezdek, J.C.[James C.], Huband, J.M.[Jacalyn M.],
Maximin Initialization for Cluster Analysis,
CIARP06(14-26).
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Franc, V.[Vojtech], Hlavác, V.[Václav],
An iterative algorithm learning the maximal margin classifier,
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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 Version. 0409
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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. IEEE Top Reference. 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.
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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.
WWW Version. 0604
Unsupervised learning; Clustering algorithms; Clustering validity BibRef

Kärkkäinen, I.[Ismo], Fränti, P.[Pasi],
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PR(40), No. 3, March 2007, pp. 784-795.
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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
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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.
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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 Link 0806
maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data 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
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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. BibRef

Franti, P.[Pasi], Virmajoki, O.[Olli], Hautamaki, V.[Ville],
Probabilistic clustering by random swap algorithm,
ICPR08(1-4).
IEEE DOI Link 0812
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Zhao, Q.[Qinpei], Hautamaki, V.[Ville], Fränti, P.[Pasi],
Knee Point Detection in BIC for Detecting the Number of Clusters,
ACIVS08(xx-yy).
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Blaschko, M.B.[Matthew B.], Lampert, C.H.[Christoph H.],
Correlational spectral clustering,
CVPR08(1-8).
IEEE DOI Link 0806
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Lampert, C.H.[Christoph H.], Blaschko, M.B.[Matthew B.],
A Multiple Kernel Learning Approach to Joint Multi-class Object Detection,
DAGM08(xx-yy).
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Zhang, Z.M.[Zi-Ming], Chan, S.[Syin], Chia, L.T.[Liang-Tien],
Discriminative Signatures for Image Classification,
ICIP07(II: 197-200).
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Discover discriminable features for classification. BibRef

Raducanu, B.[Bogdan], Vitria, J.[Jordi],
Online Learning for Human-Robot Interaction,
Learning07(1-7).
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Incremental subspace learning based on Nonparametric Discriminant Analysis. Number of classes and samples not known and changes over time. BibRef

Grim, J.[Jirí],
EM Cluster Analysis for Categorical Data,
SSPR06(640-648).
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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
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Ke, Q.[Qifa], Kanade, T.,
Robust subspace clustering by combined use of kNND metric and SVD algorithm,
CVPR04(II: 592-599).
IEEE Abstract. IEEE Top Reference. 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. IEEE Top Reference. 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. IEEE Top Reference. 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. IEEE Top Reference. 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 .


Last update:Nov 16, 2009 at 19:35:14