Shaffer, E.[Edward],
Dubes, R.C.[Richard C.],
Jain, A.K.[Anil K.],
Single-link characteristics of a mode-seeking clustering algorithm,
PR(11), No. 1, 1979, pp. 65-70.
WWW Version.
0309
BibRef
Kittler, J.V.[Josef V.],
Comments on 'single-link characteristics of a mode-seeking clustering
algorithm',
PR(11), No. 1, 1979, pp. 71-73.
WWW Version.
0309
BibRef
Pathak, A.P.[A. Pal],
Pal, S.K.,
Generalized guard-zone algorithm (GGA) for learning:
automatic selection of threshold,
PR(23), No. 3-4, 1990, pp. 325-335.
WWW Version.
0401self-supervised parameter learning.
BibRef
LeHegarat-Mascle, S.,
Bloch, I.,
Vidal-Madjar, D.,
Application of Dempster-Shafer Evidence Theory to
Unsupervised Classification in Multisource Remote Sensing,
GeoRS(35), No. 4, July 1997, pp. 1018-1031.
IEEE Top Reference.
9708 See also Mathematical Theory of Evidence, A.
BibRef
Lee, J.S.,
Grunes, M.R.,
Ainsworth, T.L.,
Du, L.J.,
Schuler, D.L.,
Cloude, S.R.,
Unsupervised Classification Using Polarimetric Decomposition and the
Complex Wishart Classifier,
GeoRS(37), No. 5, September 1999, pp. 2249.
BibRef
9909
Du, L.J.,
Grunes, M.R.,
Lee, J.S.,
Unsupervised segmentation of dual-polarization SAR images based on
amplitude and texture characteristics,
JRS(23), No. 20, October 2002, pp. 4383-4402.
WWW Version.
0211
BibRef
Brumbley, C.[Clark],
Chang, C.I.[Chein-I],
An unsupervised vector quantization-based target subspace projection
approach to mixed pixel detection and classification in unknown
background for remotely sensed imagery,
PR(32), No. 7, July 1999, pp. 1161-1174.
WWW Version.
BibRef
9907
Ren, H.,
Chang, C.I.[Chein-I],
A Generalized Orthogonal Subspace Projection Approach to Unsupervised
Multispectral Image Classification,
GeoRS(38), No. 6, November 2000, pp. 2515-2528.
IEEE Top Reference.
0011 See also Anomaly detection and classification for hyperspectral imagery.
BibRef
Chang, C.I.,
Orthogonal Subspace Projection (OSP) Revisited:
A Comprehensive Study and Analysis,
GeoRS(43), No. 3, March 2005, pp. 502-518.
IEEE Abstract. IEEE Top Reference.
0501
See also Comments on Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis.
BibRef
Roberts, S.J.[Stephen J.],
Holmes, C.[Chris],
Denison, D.[Dave],
Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain
Monte Carlo,
PAMI(23), No. 8, August 2001, pp. 909-914.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0109In unsupervised classifications, how to find the best partition.
Hence, use entropy measures.
BibRef
Gokcay, E.[Erhan],
Principe, J.C.[Jose C.],
Information Theoretic Clustering,
PAMI(24), No. 2, February 2002, pp. 158-171.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0202Applied to MRI data.
Derived from Renyi's measure.
See also On Measures of Entropy and Information.
BibRef
Yeung, D.S.,
Wang, X.Z.,
Improving Performance of Similarity-Based Clustering by Feature Weight
Learning,
PAMI(24), No. 4, April 2002, pp. 556-561.
IEEE Abstract. IEEE Top Reference.
WWW Version.
0204learning feature weights for classification.
See also Improving Fuzzy C-Means Clustering Based on Feature-Weight Learning.
BibRef
Duda, T.,
Canty, M.,
Unsupervised Classification of Satellite Imagery:
Choosing a Good Algorithm,
JRS(23), No. 11, June 2002, pp. 2193-2212.
WWW Version.
0206
BibRef
Garai, G.[Gautam],
Chaudhuri, B.B.,
A novel genetic algorithm for automatic clustering,
PRL(25), No. 2, January 2004, pp. 173-187.
WWW Version.
0401
BibRef
Frigui, H.[Hichem],
Nasraoui, O.[Olfa],
Unsupervised learning of prototypes and attribute weights,
PR(37), No. 3, March 2004, pp. 567-581.
WWW Version.
0401
BibRef
Wu, S.[Sitao],
Chow, T.W.S.[Tommy W. S.],
Clustering of the self-organizing map using a clustering validity index
based on inter-cluster and intra-cluster density,
PR(37), No. 2, February 2004, pp. 175-188.
WWW Version.
0311
BibRef
He, C.[Chao],
Girolami, M.[Mark],
Novelty detection employing an L2 optimal non-parametric density
estimator,
PRL(25), No. 12, September 2004, pp. 1389-1397.
WWW Version.
0409Reduced set density estimator.
Binary classification.
BibRef
Tasoulis, D.K.,
Vrahatis, M.N.,
Unsupervised clustering on dynamic databases,
PRL(26), No. 13, 1 October 2005, pp. 2116-2127.
WWW Version.
0509
BibRef
Yang, M.S.[Miin-Shen],
Wu, K.L.[Kuo-Lung],
Unsupervised possibilistic clustering,
PR(39), No. 1, January 2006, pp. 5-21.
WWW Version.
0512
BibRef
Wu, K.L.[Kuo-Lung],
Yang, M.S.[Miin-Shen],
Mean shift-based clustering,
PR(40), No. 11, November 2007, pp. 3035-3052.
WWW Version.
0707kernel functions; Mean shift; Robust clustering;
Generalized Epanechnikov kernel; Bandwidth selection;
Parameter estimation; Mountain method; Noise
See also Alternative c-means clustering algorithms.
BibRef
Goldberger, J.[Jacob],
Gordon, S.,
Greenspan, H.K.[Hayit K.],
Unsupervised Image-Set Clustering Using an Information Theoretic
Framework,
IP(15), No. 2, February 2006, pp. 449-458.
WWW Version.
0602
BibRef
Earlier: A2, A3, A1:
Applying the information bottleneck principle to unsupervised
clustering of discrete and continuous image representations,
ICCV03(370-377).
WWW Version.
0311
BibRef
Earlier: A1, A3, A2:
Unsupervised Image Clustering Using the Information Bottleneck Method,
DAGM02(158 ff.).
HTML Version.
0303
BibRef
Johnson, S.,
Comments on 'Orthogonal Subspace Projection (OSP) Revisited: A
Comprehensive Study and Analysis',
GeoRS(45), No. 2, February 2007, pp. 532-533.
WWW Version.
0703
See also Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis.
BibRef
Xiang, T.[Tao],
Gong, S.G.[Shao-Gang],
Spectral clustering with eigenvector selection,
PR(41), No. 3, March 2008, pp. 1012-1029.
WWW Version.
0711
BibRef
Earlier:
Visual Learning Given Sparse Data of Unknown Complexity,
ICCV05(I: 701-708).
WWW Version.
0510Spectral clustering; Feature selection; Unsupervised learning;
Image segmentation; Video behaviour pattern clustering
BibRef
Camci, F.[Fatih],
Chinnam, R.B.[Ratna Babu],
General support vector representation machine for one-class
classification of non-stationary classes,
PR(41), No. 10, October 2008, pp. 3021-3034.
WWW Version.
0808Novelty detection; One-class classification; Support vector machine;
Non-stationary classes; Non-stationary processes; Online training;
Outlier detection
BibRef
Liu, J.G.[Jin-Gen],
Shah, M.[Mubarak],
Scene Modeling Using Co-Clustering,
ICCV07(1-7).
WWW Version.
0710Bag of Visterms (BOV).
Group by similar concept.
BibRef
Inoue, K.[Kohei],
Urahama, K.[Kiichi],
Hierarchically Distributed Dynamic Mean Shift,
ICIP07(I: 269-272).
WWW Version.
0709Iterative mode seeking algorithm. A less memory intensive implementation.
BibRef
Guan, L.[Ling],
Self-Organizing Trees and Forests:
A Powerful Tool in Pattern Clustering and Recognition,
ICIAR06(I: 1-14).
WWW Version.
0610
BibRef
Kyan, M.[Matthew],
Guan, L.[Ling],
Local Variance Driven Self-Organization for Unsupervised Clustering,
ICPR06(III: 421-424).
WWW Version.
0609
BibRef
Lange, T.[Tilman],
Law, M.H.C.[Martin H.C.],
Jain, A.K.[Anil K.],
Buhmann, J.M.[Joachim M.],
Learning with Constrained and Unlabelled Data,
CVPR05(I: 731-738).
WWW Version.
0507
BibRef
Furao, S.[Shen],
Hasegawa, O.[Osamu],
An On-Line Learning Mechanism for Unsupervised Classification and
Topology Representation,
CVPR05(I: 651-656).
WWW Version.
0507
BibRef
Robles-Kelly, A.,
Hancock, E.R.,
Pairwise Clustering with Matrix Factorisation and the EM Algorithm,
ECCV02(II: 63 ff.).
HTML Version.
0205for grouping via pairwise clustering.
BibRef
Zhu, Y.,
Comaniciu, D.,
Schwartz, S.,
Ramesh, V.,
Multimodal Data Representations with Parameterized Local Structures,
ECCV02(I: 173 ff.).
HTML Version.
0205
BibRef
Boujemaa, N.[Nozha],
On Competitive Unsupervised Clustering,
ICPR00(Vol I: 631-634).
WWW Version.
HTML Version.
0009For segmentation.
BibRef
Nowak, R.D.,
Figueiredo, M.A.T.,
Unsupervised Segmentation of Poisson Data,
ICPR00(Vol III: 155-158).
WWW Version.
HTML Version.
0009
BibRef
Stauffer, C.[Chris],
Minimally-supervised classification using multiple observation sets,
ICCV03(297-304).
WWW Version.
0311
BibRef
Stauffer, C.[Chris],
Minimally Supervised Classification,
DARPA98(145-150).
BibRef
9800
Fränti, P.,
Kivijärvi, J.,
Random Swapping Technique for Improving Clustering in Unsupervised
Classification,
SCIA99(Pattern Recognition I).
BibRef
9900
Descombes, X.,
Kruggel, F.,
Palubinskas, G.[Gintautas],
An Unsupervised Clustering Method Using the Entropy Minimization,
ICPR98(Vol II: 1816-1818).
WWW Version.
9808
BibRef
Renyi, A.,
On Measures of Entropy and Information,
ConferenceBerkeley Symposium Mathematics, Statistics, and Probability, 1960, pp. 547-561.
BibRef
6000
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Iterative, Hierarchical Clustering Techniques .