Pospisil, A.,
AMOS: the 'learning' multiclass pattern classifier,
PR(3), No. 3, October 1971, pp. 253-268.
WWW Version.
0309
BibRef
Dubes, R.C.[Richard C.],
How many clusters are best? - An experiment,
PR(20), No. 6, 1987, pp. 645-663.
WWW Version.
0309
BibRef
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
BibRef
Soklic, M.E.[Milan E.],
Adaptive model for decision making,
PR(15), No. 6, 1982, pp. 485-493.
WWW Version.
0309
BibRef
Soklic, M.E.[Milan E.],
The partition of N-dimensional space, using shells,
PR(17), No. 3, 1984, pp. 339-344.
WWW Version.
0309
BibRef
di Gesù, V.,
Sacco, B.,
Some statistical properties of the minimum spanning forest,
PR(16), No. 5, 1983, pp. 525-531.
WWW Version.
0309Finds 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
BibRef
Zhang, Q.W.[Qi-Wen],
Wang, Q.R.[Qing Ren],
Knowledge based ISOETRP clustering procedure,
ICPR88(II: 1233-1235).
WWW Version.
8811
BibRef
Jolion, J.M.[Jean-Michel],
Rosenfeld, A.[Azriel],
Cluster Detection in Background Noise,
PR(22), No. 5, 1989, pp. 603-607.
WWW Version.
0309
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
BibRef
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
BibRef
Herbin, M.,
Bonnet, N.,
Vautrot, P.,
Estimation of the number of clusters and influence zones,
PRL(22), No. 14, December 2001, pp. 1557-1568.
HTML Version.
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. IEEE Top Reference.
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
BibRef
Earlier:
A Robust Clustering Algorithm Based on Competitive Agglomeration and
Soft Rejection of Outliers,
CVPR96(550-555).
IEEE Abstract. IEEE Top Reference.
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
BibRef
Frigui, H.[Hichem],
Membershipmap: data transformation based on membership aggregation,
ICPR04(II: 463-466).
WWW Version.
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.
0707Relational 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. IEEE Top Reference.
0209minimize 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).
WWW Version.
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
BibRef
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.
0409Alternative 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.
WWW Version.
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.
0604Unsupervised 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.
0611Clustering; 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).
WWW Version.
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.
0711Spectral 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.
0711Markov 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.
WWW Version.
0806maximizes a generalization of the Wilcoxon-Mann-Whitney statistic
on the training data
BibRef
Raducanu, B.[Bogdan],
Vitria, J.[Jordi],
Online Learning for Human-Robot Interaction,
Learning07(1-7).
WWW Version.
0706Incremental 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).
WWW Version.
0608Sequential 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).
WWW Version.
0608A 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).
WWW Version.
0606Turn into a clustering problem.
BibRef
Nasios, N.[Nikolaos],
Bors, A.G.[Adrian G.],
Finding the Number of Clusters for Nonparametric Segmentation,
CAIP05(213).
WWW Version.
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).
WWW Version.
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. IEEE Top Reference.
0408Kth-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.
0408Cluster 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).
WWW Version.
BibRef
9600
Wallace, R.S., and
Kanade, T.,
Finding Natural Clusters Having Minimal Description Lengths,
ICPR90(I: 438-442).
WWW Version.
BibRef
9000
Bandapadhay, A.,
Fu, J.L.,
Searching parameter spaces with noisy linear constraints,
CVPR88(550-555).
IEEE Abstract. IEEE Top Reference.
0403predicated 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 .