14.2.4 Iterative, Hierarchical Clustering Techniques

Zobrist, A.L.[Albert L.],
The organization of extracted features for pattern recognition,
PR(3), No. 1, April 1971, pp. 23-30.
WWW Version. 0309A two layered pattern recognition program. The first layer scans an input for features and produces a coded representation. The second layer looks for combinations of code which signify relations between features in the input. BibRef

Haralick, R.M.[Robert M.], Dinstein, I.,
An Iterative Clustering Procedure,
), SMC(1), No. 3, July, 1971, pp. 275-289. BibRef 7107

Biehl, L.L., and Silva, L.F.,
A Multilevel Multispectral Data Set Analysis in the Visible and Infrared Wavelength Regions,
PIEEE(63), No. 1, 1975, pp. 164-175. BibRef 7500

Lukasová, A.[Alena],
Hierarchical agglomerative clustering procedure,
PR(11), No. 5-6, 1979, pp. 365-381.
WWW Version. 0309 BibRef

Smith, S.P.[Stephen P.], Dubes, R.C.[Richard C.],
Stability of a hierarchical clustering,
PR(12), No. 3, 1980, pp. 177-187.
WWW Version. 0309 BibRef

Ozawa, K.[Kazumasa],
Classic: A hierarchical clustering algorithm based on asymmetric similarities,
PR(16), No. 2, 1983, pp. 201-211.
WWW Version. 0309 BibRef

Ozawa, K.[Kazumasa],
A stratificational overlapping cluster scheme,
PR(18), No. 3-4, 1985, pp. 279-286.
WWW Version. 0309 BibRef

Perruchet, C.[Christophe],
Constrained agglomerative hierarchical classification,
PR(16), No. 2, 1983, pp. 213-217.
WWW Version. 0309 BibRef

Schuermann, J.[Juergen], Doster, W.[Wolfgang],
A decision theoretic approach to hierarchical classifier design,
PR(17), No. 3, 1984, pp. 359-369.
WWW Version. 0309 BibRef

de Mŕntaras, R.L.[R. López], Aguilar-Martín, J.,
Self-learning pattern classification using a sequential clustering technique,
PR(18), No. 3-4, 1985, pp. 271-277.
WWW Version. 0309 BibRef

Jain, N.C.[Naresh C.], Indrayan, A.[Abhaya], Goel, L.R.[Lajpat R.],
Monte Carlo comparison of six hierarchical clustering methods on random data,
PR(19), No. 1, 1986, pp. 95-99.
WWW Version. 0309 BibRef

Plastria, F.[Frank],
Two hierarchies associated with each clustering scheme,
PR(19), No. 2, 1986, pp. 193-196.
WWW Version. 0309 BibRef

Kurzynski, M.W.[Marek W.],
On the Identity of Optimal Strategies for Multistage Classifiers,
PRL(10), No. 1, 1989, pp. 39-46. See also optimal strategy of a tree classifier, The. See also On the multistage Bayes classifier. BibRef 8900

Kurzynski, M.W., Puchala, E., Blinowska, A.,
A branch-and-bound algorithm for optimization of multiperspective classifier,
ICPR94(B:235-239).
IEEE DOI Reference 9410 BibRef

Kurzynski, M.W., Puchala, E.,
Algorithms of the multiperspective pattern recognition,
ICPR92(II:627-630).
IEEE DOI Reference 9208 BibRef

Spivak, S.[Shalom],
A multisurface method for pattern classification,
PR(22), No. 5, 1989, pp. 587-591.
WWW Version. 0309The desired classifier is built in two stages. In the first stage an initial classifier is built. In the second stage the classifier is modified to obtain the desired properties. Modification is performed by use of the gradient descent procedure. BibRef

Spivak, S.[Shalom],
Some properties of the multisurface method for pattern classification,
PR(24), No. 4, 1991, pp. 325-330.
WWW Version. 0401 BibRef

Li, X.,
Parallel algorithms for hierarchical clustering and cluster validity,
PAMI(12), No. 11, November 1990, pp. 1088-1092.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Murtagh, F.,
Comments on 'Parallel algorithms for hierarchical clustering and cluster validity',
PAMI(14), No. 10, October 1992, pp. 1056-1057.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0401 BibRef

Zhang, Q.W.[Qi-Wen], Boyle, R.D.[Roger D.],
A new clustering algorithm with multiple runs of iterative procedures,
PR(24), No. 9, 1991, pp. 835-848.
WWW Version. 0401 BibRef

Chan, K.P., Cheung, Y.S.,
Clustering of clusters,
PR(25), No. 2, February 1992, pp. 211-217.
WWW Version. 0401 BibRef

Liaw, J.N.[Jin-Nan], Kashyap, R.L.,
A new sequential classifier using information criterion window,
PR(27), No. 10, October 1994, pp. 1423-1438.
WWW Version. 0401 BibRef

Zavaljevski, A., Dhawan, A.P., Kelch, D.J., Riddell, J.,
Adaptive Multilevel Classification and Detection in Multispectral Images,
OptEng(35), No. 10, October 1996, pp. 2884-2893. 9611 BibRef

Bishop, C.M., Tipping, M.E.,
A Hierarchical Latent Variable Model For Data Visualization,
PAMI(20), No. 3, March 1998, pp. 281-293.
IEEE Abstract. IEEE Top Reference.
WWW Version. 9805Visualize the data. BibRef

El-Sonbaty, Y.[Yasser], Ismail, M.A.,
On-line hierarchical clustering,
PRL(19), No. 14, December 1998, pp. 1285-1291. BibRef 9812

Fisher, D.,
Iterative Optimization and Simplification of Hierarchical Clusterings,
JAIR(4), 1996, pp. 147-178.
HTML Version. BibRef 9600

Labonté, G.,
On a Neural Network that Performs an Enhanced Nearest-Neighbour Matching,
PAA(3), No. 3 2000, pp. 267-278. 0010 BibRef

Leung, Y.[Yee], Zhang, J.S.[Jiang-She], Xu, Z.B.[Zong-Ben],
Clustering by Scale-Space Filtering,
PAMI(22), No. 12, December 2000, pp. 1396-1410.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0012 BibRef

Leung, Y.[Yee], Ma, J.H.[Jiang-Hong], Zhang, W.X.[Wen-Xiu],
A New Method for Mining Regression Classes in Large Data Sets,
PAMI(23), No. 1, January 2001, pp. 5-21.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0101Genetic algorithm. Regression class: subset of data subject to regression model. BibRef

Talavera, L.[Luis], Béjar, J.[Javier],
Generality-Based Conceptual Clustering with Probabilistic Concepts,
PAMI(23), No. 2, February 2001, pp. 196-206.
IEEE Abstract. IEEE Top Reference.
WWW Version. 0102 BibRef

Fieguth, P.W.[Paul W.],
Multiply-rooted multiscale models for large-scale estimation,
IP(10), No. 11, November 2001, pp. 1676-1686.
IEEE DOI Reference 0201 BibRef
Earlier:
Foveated Multiscale Models for Large-Scale Estimation,
ICIP99(II:871-874).
IEEE Abstract. IEEE Top Reference. BibRef
Earlier:
Multipole-motivated reduced-state estimation,
ICIP98(I: 635-638).
IEEE DOI Reference 9810 BibRef

Agnelli, D.[Davide], Bollini, A.[Alessandro], Lombardi, L.[Luca],
Image classification: an evolutionary approach,
PRL(23), No. 1-3, January 2002, pp. 303-309.
HTML Version. 0201 BibRef

Sun, Y.[Ying], Zhu, Q.M.[Qiu-Ming], Chen, Z.X.[Zheng-Xin],
An iterative initial-points refinement algorithm for categorical data clustering,
PRL(23), No. 7, May 2002, pp. 875-884.
HTML Version. 0203 BibRef

Lam, W.[Wai], Keung, C.K.[Chi-Kin], Liu, D.Y.[Dan-Yu],
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction,
PAMI(24), No. 8, August 2002, pp. 1075-1090.
IEEE Abstract. IEEE Top Reference. 0208ICPL. Integrated Concept Prototype Learner. Integrate instance filtering with instace abstraction. BibRef

Fernández Prieto, D.,
An iterative approach to partially supervised classification problems,
JRS(23), No. 18, September 2002, pp. 3887-3892.
WWW Version. 0211 See also adaptive semiparametric and context-based approach to unsupervised change detection multitemporal remote-sensing images, An. BibRef

Rodríguez, C., Soraluze, I., Muguerza, J., Martín, J.I., Álvarez, G.,
Hierarchical classifiers based on neighbourhood criteria with adaptive computational cost,
PR(35), No. 12, December 2002, pp. 2761-2769.
WWW Version. 0209 BibRef

Vijaya, P.A., Murty, M.N.[M. Narasimha], Subramanian, D.K.,
Leaders-Subleaders: An efficient hierarchical clustering algorithm for large data sets,
PRL(25), No. 4, March 2004, pp. 505-513.
WWW Version. 0402 BibRef

Huber, R.[Reinhold], Ramoser, H.[Herbert], Mayer, K.[Konrad], Penz, H.[Harald], Rubik, M.[Michael],
Classification of coins using an eigenspace approach,
PRL(26), No. 1, 1 January 2005, pp. 61-75.
WWW Version.
WWW Version. 0501Multistage classifier for large classes of coins. BibRef

Hill, E.J.[E. June], Alder, M.D.[Michael D.], de Silva, C.J.S.[Christopher J.S.],
An improvement to the DR clustering algorithm,
PRL(26), No. 1, 1 January 2005, pp. 101-107.
WWW Version. 0501Map data from a region to toroidal surface then run DR (Dog-Rabbit) clustering. BibRef

Lee, S., Crawford, M.M.,
Unsupervised Multistage Image Classification Using Hierarchical Clustering With a Bayesian Similarity Measure,
IP(14), No. 3, March 2005, pp. 312-320.
IEEE DOI Reference 0501 BibRef

Milgram, J.[Jonathan], Sabourin, R.[Robert], Cheriet, M.[Mohamed],
Combining Model-based and Discriminative Approaches in a Modular Two-stage Classification System: Application to Isolated Handwritten Digit Recognition,
ELCVIA(5), No. 2, 2005, pp. 1-15.
WWW Version. 0505 BibRef
Earlier:
Two-stage classification system combining model-based and discriminative approaches,
ICPR04(I: 152-155).
IEEE DOI Reference 0409 BibRef

Lee, J.W.T.[John W.T.], Yeung, D.S.[Daniel S.], Tsang, E.C.C.[Eric C.C.],
Hierarchical clustering based on ordinal consistency,
PR(38), No. 11, November 2005, pp. 1913-1925.
WWW Version. 0509 BibRef

Wang, D., Yeung, D.S.[Daniel S.], Tsang, E.C.C.[Eric C.C.],
Structured One-Class Classification,
SMC-B(36), No. 6, December 2006, pp. 1283-1295.
IEEE DOI Reference 0701 BibRef

Amador, J.J.[José J.],
Sequential clustering by statistical methodology,
PRL(26), No. 14, 15 October 2005, pp. 2152-2163.
WWW Version. 0510 BibRef

Dutta, M., Mahanta, A.K.[A. Kakoti], Pujari, A.K.[Arun K.],
QROCK: A quick version of the ROCK algorithm for clustering of categorical data,
PRL(26), No. 15, November 2005, pp. 2364-2373.
WWW Version. 0510An agglomerative hierarchical clustering algorithm for clustering categorical data. BibRef

Zilong, G.[Guo], Sun'an, W.[Wang], Jian, Z.[Zhuang],
A novel immune evolutionary algorithm incorporating chaos optimization,
PRL(27), No. 1, 1 January 2006, pp. 2-8.
WWW Version. 0512 BibRef

Fränti, P.[Pasi], Virmajoki, O.[Olli],
Iterative shrinking method for clustering problems,
PR(39), No. 5, May 2006, pp. 761-775.
WWW Version. Vector quantization; Codebook generation; Agglomeration; PNN 0604 BibRef
Earlier: A2, A1:
Divide-and-conquer algorithm for creating neighborhood graph for clustering,
ICPR04(I: 264-267).
IEEE DOI Reference 0409 BibRef

Virmajoki, O., Franti, P., Kaukoranta, T.,
Iterative shrinking method for generating clustering,
ICIP02(II: 685-688).
IEEE Abstract. IEEE Top Reference. 0210 BibRef

Nock, R.[Richard], and Nielsen, F.[Frank],
On Weighting Clustering,
PAMI(28), No. 8, August 2006, pp. 1223-1235.
IEEE DOI Reference 0606 BibRef
Earlier:
Improving clustering algorithms through constrained convex optimization,
ICPR04(IV: 557-560).
IEEE DOI Reference 0409Formalize unsupervised clustering ideas to take advantage of boosting ideas. BibRef

Nock, R.[Richard], Vaillant, P.[Pascal], Henry, C.[Claudia], Nielsen, F.[Frank],
Soft memberships for spectral clustering, with application to permeable language distinction,
PR(42), No. 1, January 2009, pp. 43-53.
WWW Version. 0809Spectral clustering; Soft membership; Stochastic processes; Text classification BibRef

Kushnir, D.[Dan], Galun, M.[Meirav], Brandt, A.[Achi],
Fast multiscale clustering and manifold identification,
PR(39), No. 10, October 2006, pp. 1876-1891.
WWW Version. 0606Algebraic multigrid (AMG); Aggregation; Graph partitioning; Similarity-based clustering; Manifold; Data analysis; Astrophysical models BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Astigarraga, A.,
Classifier hierarchy learning by means of genetic algorithms,
PRL(27), No. 16, December 2006, pp. 1998-2004.
WWW Version. 0611Data mining; Classifier combination; Genetic algorithms BibRef

Martínez-Otzeta, J.M., Sierra, B., Lazkano, E., Jauregi, E., Yurramendi, Y.,
Analyzing Classifier Hierarchy Multiclassifier Learning,
CIARP08(775-782).
Springer DOI Reference 0809 BibRef

Cao, F.[Frédéric], Delon, J.[Julie], Desolneux, A.[Agnčs], Musé, P.[Pablo], Sur, F.[Frédéric],
A Unified Framework for Detecting Groups and Application to Shape Recognition,
JMIV(27), No. 2, February 2007, pp. 91-119.
Springer DOI Reference 0704 See also grouping principle and four applications, A. Evaluate validity of clusters, containment of clusters, merging of clusters. BibRef

Santos, J.M.[Jorge M.], de Sa, J.M.[Joaquim Marques], Alexandre, L.A.[Luis A.],
LEGClust: A Clustering Algorithm Based on Layered Entropic Subgraphs,
PAMI(30), No. 1, January 2008, pp. 62-75.
IEEE DOI Reference 0711Builds layers of subgraphs then applies clustering. BibRef

Goldberger, J.[Jacob], Tassa, T.[Tamir],
A hierarchical clustering algorithm based on the Hungarian method,
PRL(29), No. 11, 1 August 2008, pp. 1632-1638.
WWW Version. 0804Grouping; Pairwise clustering; Hierarchical clustering; Graph algorithms BibRef

Aghagolzadeh, M., Soltanian-Zadeh, H., Araabi, B., Aghagolzadeh, A.,
A Hierarchical Clustering Based on Mutual Information Maximization,
ICIP07(I: 277-280).
IEEE DOI Reference 0709 BibRef

Roth, V.[Volker], Fischer, B.[Bernd],
The kernelHMM: Learning Kernel Combinations in Structured Output Domains,
DAGM07(436-445).
Springer DOI Reference 0709 BibRef

Sakai, T.[Tomoya], Komazaki, T.[Takuto], Imiya, A.[Atsushi],
Scale-Space Clustering with Recursive Validation,
SSVM07(288-299).
Springer DOI Reference 0705 BibRef

Akbas, E.[Emre], Vural, F.T.Y.[Fatos T. Yarman],
Automatic Image Annotation by Ensemble of Visual Descriptors,
SLAM07(1-8).
IEEE DOI Reference 0706Do not just add all the features, due to different types, redundancy. Each feature learned at lowest level, combined at other levels. BibRef

Batra, D., Sukthankar, R., Chen, T.,
Semi-Supervised Clustering via Learnt Codeword Distances,
BMVC08(xx-yy).
PDF Version. 0809 BibRef

Yang, L.[Liu], Jin, R.[Rong], Pantofaru, C.[Caroline], Sukthankar, R.[Rahul],
Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data,
CVPR07(1-8).
IEEE DOI Reference 0706 BibRef

Pantofaru, C., Hebert, M.,
A framework for learning to recognize and segment object classes using weakly supervised training data,
BMVC07(xx-yy).
PDF Version. 0709 BibRef

Monteleoni, C.[Claire], Kaariainen, M.[Matti],
Practical Online Active Learning for Classification,
Learning07(1-8).
IEEE DOI Reference 0706 BibRef

Gagrani, A.[Aakanksha], Gupta, L.[Lalit], Ravindran, B., Das, S.[Sukhendu], Roychowdhury, P.[Pinaki], Panchal, V.K.,
A Hierarchical Approach to Landform Classification of Satellite Images Using a Fusion Strategy,
ICCVGIP06(140-151).
Springer DOI Reference 0612First into 3 major landforms, then classify within each of these. BibRef

Sternby, J.[Jakob],
Class Dependent Cluster Refinement,
ICPR06(II: 833-836).
WWW Version. 0609 BibRef

Lam, B.S.Y.[Benson S. Y.], Yan, H.[Hong],
Improved Clustering Algorithm Based on Calculus of Variation,
ICPR06(I: 900-903).
WWW Version. 0609 BibRef

Prehn, H.[Herward], Sommer, G.[Gerald],
An Adaptive Classification Algorithm Using Robust Incremental Clustering,
ICPR06(I: 896-899).
WWW Version. 0609 BibRef

Azran, A.[Arik], Ghahramani, Z.[Zoubin],
Spectral Methods for Automatic Multiscale Data Clustering,
CVPR06(I: 190-197).
IEEE DOI Reference 0606 BibRef

Zhang, K.[Kai], Tang, M.[Ming], Kwok, J.T.[James T.],
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation,
CVPR05(II: 1001-1007).
IEEE DOI Reference 0507 BibRef

Carrivick, L.[Luke], Prabhu, S.[Sanjay], Goddard, P.[Paul], Rossiter, J.[Jonathan],
Unsupervised Learning in Radiology Using Novel Latent Variable Models,
CVPR05(II: 854-859).
IEEE DOI Reference 0507 BibRef

Bouvrie, J.V.[Jake V.],
Multiple Resolution Image Classification,
MIT AIMAIM-2002-022, December 2002.
WWW Version. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. 0306 BibRef

Shi, S.[Shuming], Yang, G.[Guangwen], Wang, D.X.[Ding-Xing], Zheng, W.M.[Wei-Min],
Potential-based hierarchical clustering,
ICPR02(IV: 272-275).
IEEE DOI Reference 0211 BibRef

Rendon, E., Barandela, R.,
Fast hierarchical clustering based on compressed data,
ICPR02(II: 216-219).
IEEE DOI Reference 0211 BibRef

Zöller, T., Buhmann, J.M.,
Active Learning for Hierarchical Pairwise Data Clustering,
ICPR00(Vol II: 186-189).
IEEE DOI Reference
HTML Version. 0009 BibRef

Chardin, A., Perez, P.,
Unsupervised Image Classification with a Hierarchical EM Algorithm,
ICCV99(969-974).
IEEE DOI Reference BibRef 9900
Earlier:
Semi-iterative inference with hierarchical models,
ICIP98(I: 630-634).
IEEE DOI Reference 9810 BibRef

Li, J.[Jia], Gray, R.M.,
Context based multiscale classification of images,
ICIP98(III: 566-570).
IEEE DOI Reference 9810 BibRef

Schikuta, E.,
Grid-Clustering: An Efficient Hierarchical Clustering Method for Very Large Data Sets,
ICPR96(II: 101-105).
IEEE DOI Reference 9608(Univ. of Vienna, A) BibRef

Bajcsy, P., Ahuja, N.,
Uniformity and Homogeneity Based Hierarchical Clustering,
ICPR96(II: 96-100).
IEEE DOI Reference 9608(Univ. of Illinois, Urbana, USA) BibRef

Roberts, S.J.,
Scale-Space Unsupervised Cluster Analysis,
ICPR96(II: 106-110).
IEEE DOI Reference 9608(Univ. of London, UK) BibRef

Jin, J.S.,
Hierarchical pattern matching using a high entropy signature,
ICPR94(B:436-438).
IEEE DOI Reference 9410 BibRef

Prabhu, S.M., Garg, D.P., Spano, Sr., M.R.,
A hierarchical labeled object classification system,
ICPR94(B:479-481).
IEEE DOI Reference 9410 BibRef

Jiang, H.[Hongtao], Bolviken, E.,
A general parameter updating approach to image classification,
ICPR94(A:720-722).
IEEE DOI Reference 9410 BibRef

Tseng, C.T., Moret, B.M.E.,
The design of a nonparametric hierarchical classifier,
ICPR90(I: 428-432).
IEEE DOI Reference 9006 BibRef

Li, X.,
Hierarchical clustering on SIMD machines with alignment network,
CVPR89(660-665).
IEEE Abstract. IEEE Top Reference. 0403 BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Distance Measures, Criteria for Clustering .