14.2.3 Unsupervised Clustering, Classification

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. 0401
self-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. 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.
WWW Version. 0109
In 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.
WWW Version. 0202
Applied 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.
WWW Version. 0204
learning 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.A.[Mark A.],
Novelty detection employing an L2 optimal non-parametric density estimator,
PRL(25), No. 12, September 2004, pp. 1389-1397.
WWW Version. 0409
Reduced 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. 0707
kernel functions; Mean shift; Robust clustering; Generalized Epanechnikov kernel; Bandwidth selection; Parameter estimation; Mountain method; Noise See also Alternative c-means clustering algorithms. BibRef

Wu, K.L.[Kuo-Lung], Yang, M.S.[Miin-Shen], Hsieh, J.N.[June-Nan],
Robust cluster validity indexes,
PR(42), No. 11, November 2009, pp. 2541-2550.
Elsevier DOI Link
WWW Version. 0907
Cluster validity index; Clustering algorithms; Fuzzy c-means; Partition membership; Mean; Median; Robust; Noise; Outlier 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.
IEEE DOI Link 0602
BibRef
Earlier: A2, A3, A1:
Applying the information bottleneck principle to unsupervised clustering of discrete and continuous image representations,
ICCV03(370-377).
IEEE DOI Link 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.
IEEE DOI Link 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).
IEEE DOI Link 0510
Spectral 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. 0808
Novelty detection; One-class classification; Support vector machine; Non-stationary classes; Non-stationary processes; Online training; Outlier detection BibRef

Wu, M.R.[Ming-Rui], Ye, J.P.[Jie-Ping],
A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers,
PAMI(31), No. 11, November 2009, pp. 2088-2092.
IEEE DOI Link 0910
BibRef

Xavier, A.E.[Adilson Elias],
The hyperbolic smoothing clustering method,
PR(43), No. 3, March 2010, pp. 731-737.
Elsevier DOI Link
WWW Version. 1001
Cluster analysis; Min-sum-min problems; Nondifferentiable programming; Smoothing BibRef

Cruz, B.[Benjamín], Barrón, R.[Ricardo], Sossa, H.[Humberto],
A New Unsupervised Learning for Clustering Using Geometric Associative Memories,
CIARP09(239-246).
Springer DOI Link 0911
BibRef

Gilani, Z.[Zulqarnain], Rao, N.I.[Naveed Iqbal],
Fast Block Clustering Based Optimized Adaptive Mediod Shift,
CAIP09(435-443).
Springer DOI Link 0909
BibRef

Joshi, A.J.[Ajay J.], Porikli, F.M.[Fatih M.], Papanikolopoulos, N.P.[Nikolaos P.],
Multi-class active learning for image classification,
CVPR09(2372-2379).
IEEE DOI Link 0906
BibRef

Singh, A.[Abhishek], Jaikumar, P.[Padmini], Mitra, S.K.[Suman K.],
A Bayesian Learning Based Approach for Clustering of Satellite Images,
ICCVGIP08(187-192).
IEEE DOI Link 0812
BibRef

Sudo, K.[Kyoko], Osawa, T.[Tatsuya], Tanaka, H.[Hidenori], Koike, H.[Hideki], Arakawa, K.[Kenichi],
Online anomal movement detection based on unsupervised incremental learning,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Chang, Y.C.[Yu-Chou], Lee, D.J.[Dah-Jye], Archibald, J.[James], Hong, Y.[Yi],
Unsupervised clustering using hyperclique pattern constraints,
ICPR08(1-4).
IEEE DOI Link 0812
BibRef

Bai, X.X.[Xin-Xin], Chen, G.[Gang], Lin, Z.L.[Zhong-Lin], Yin, W.J.[Wen-Jun], Dong, J.[Jin],
Improving image clustering: An unsupervised feature weight learning framework,
ICIP08(977-980).
IEEE DOI Link 0810
BibRef

Vatsavai, R.R.[Ranga Raju], Shekhar, S.[Shashi], Bhaduri, B.[Budhendra],
A Learning Scheme for Recognizing Sub-classes from Model Trained on Aggregate Classes,
SSPR08(967-976).
Springer DOI Link 0812
Aggreate labels, not all sub-classes. BibRef

Gibert, K.[Karina], Rodríguez Silva, G.[Gustavo],
Identification of More Characteristic Dynamic Patterns in a WWTP by CIBRxE,
CIARP08(372-380).
Springer DOI Link 0809
BibRef

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Classification via semi-Riemannian spaces,
CVPR08(1-8).
IEEE DOI Link 0806
BibRef
Earlier:
Contextual Distance for Data Perception,
ICCV07(1-8).
IEEE DOI Link 0710
Context from nearest neighbors. BibRef

Liu, J.G.[Jin-Gen], Shah, M.[Mubarak],
Scene Modeling Using Co-Clustering,
ICCV07(1-7).
IEEE DOI Link 0710
Bag of Visterms (BOV). Group by similar concept. BibRef

Inoue, K.[Kohei], Urahama, K.[Kiichi],
Hierarchically Distributed Dynamic Mean Shift,
ICIP07(I: 269-272).
IEEE DOI Link 0709
Iterative 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).
Springer DOI Link 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).
IEEE DOI Link 0507
BibRef

Furao, S.[Shen], Hasegawa, O.[Osamu],
An On-Line Learning Mechanism for Unsupervised Classification and Topology Representation,
CVPR05(I: 651-656).
IEEE DOI Link 0507
BibRef

Robles-Kelly, A., Hancock, E.R.,
Pairwise Clustering with Matrix Factorisation and the EM Algorithm,
ECCV02(II: 63 ff.).
HTML Version. 0205
for 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).
IEEE DOI Link
HTML Version. 0009
For segmentation. BibRef

Nowak, R.D., Figueiredo, M.A.T.,
Unsupervised Segmentation of Poisson Data,
ICPR00(Vol III: 155-158).
IEEE DOI Link
HTML Version. 0009
BibRef

Stauffer, C.[Chris],
Minimally-supervised classification using multiple observation sets,
ICCV03(297-304).
IEEE DOI Link 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).
IEEE DOI Link 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
Semi-Supervised Clustering, Classification .