14.2.9 Detecting Clusters and Number of Clusters, Number of Classes

Pospisil, A.,
AMOS: the 'learning' multiclass pattern classifier,
PR(3), No. 3, October 1971, pp. 253-268.
Elsevier DOI 0309
Adaptive thresholds. BibRef

Dubes, R.C.[Richard C.],
How many clusters are best? - An experiment,
PR(20), No. 6, 1987, pp. 645-663.
Elsevier DOI 0309
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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.
Elsevier DOI 0309
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Adaptive model for decision making,
PR(15), No. 6, 1982, pp. 485-493.
Elsevier DOI 0309
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Soklic, M.E.[Milan E.],
The partition of N-dimensional space, using shells,
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Elsevier DOI 0309
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di Gesù, V., Sacco, B.,
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PR(16), No. 5, 1983, pp. 525-531.
Elsevier DOI 0309
Finds 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.
Elsevier DOI 0309
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Suen, C.Y., Wang, Q.R.,
ISOETRP: An interactive clustering algorithm with new objectives,
PR(17), No. 2, 1984, pp. 211-219.
Elsevier DOI 0309
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Zhang, Q.W.[Qi-Wen], Wang, Q.R.[Qing Ren],
Knowledge based ISOETRP clustering procedure,
ICPR88(II: 1233-1235).
IEEE DOI 8811
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Jolion, J.M.[Jean-Michel], Rosenfeld, A.[Azriel],
Cluster Detection in Background Noise,
PR(22), No. 5, 1989, pp. 603-607.
Elsevier DOI 0309
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Sher, C.A., Rosenfeld, A.,
Pyramid Cluster Detection and Delineation by Consensus,
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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.
Elsevier DOI 0401
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And:
Author's reply to comments,
PR(26), No. 7, July 1993, pp. 1121.
Elsevier DOI 0401
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Cho, T.H.[Tai-Hoon],
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Krishnapuram, R.[Raghu], Freg, C.P.[Chih-Pin],
Fitting An Unknown Number of Lines and Planes to Image Data Through Compatible Cluster Merging,
PR(25), No. 4, April 1992, pp. 385-400.
Elsevier DOI 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
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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.
Elsevier DOI 0110
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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.
IEEE DOI 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 DOI BibRef

Frigui, H.[Hichem], Krishnapuram, R.[Raghu],
Clustering by Competitive Agglomeration,
PR(30), No. 7, July 1997, pp. 1109-1119.
Elsevier DOI 9707
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Frigui, H.[Hichem],
Clustering: Algorithms and Applications,
IPTA08(1-11).
IEEE DOI 0811
BibRef

Frigui, H.[Hichem],
Membershipmap: data transformation based on membership aggregation,
ICPR04(II: 463-466).
IEEE DOI 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.
Elsevier DOI 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,
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

Tibshirani, R., Guenther, W., Hastie, T.,
Estimating the number of clusters in a data set via the gap statistic,
RoyalStat(B 63), 2001, pp. 411-423.
DOI Link BibRef 0100

Sbai, E.,
Cluster analysis by adaptive rank-order filters,
PR(34), No. 10, October 2001, pp. 2015-2027.
Elsevier DOI 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. 0209
minimize the sum-of-squared-error with a constraint on cluster variance. BibRef

Sugar, C.A., James, G.M.,
Finding the number of clusters in a data set: An information theoretic approach,
ASAJ(98), 2003, pp. 750-763.
DOI Link 1706
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.
Elsevier DOI 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.
Elsevier DOI 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.
Elsevier DOI Clustering; Similarity measures; Cluster validity; Data visualization; Scalability 0606
BibRef
Earlier:
Maximin Initialization for Cluster Analysis,
CIARP06(14-26).
Springer DOI 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.
Elsevier DOI 0307
BibRef
And:
Greedy Algorithm for a Training Set Reduction in the Kernel Methods,
CAIP03(426-433).
Springer DOI 0311
BibRef
Earlier:
Multi-class support vector machine,
ICPR02(II: 236-239).
IEEE DOI 0211
BibRef

Uricár, M.[Michal], Franc, V.[Vojtech], Hlavác, V.[Václav],
Bundle Methods for Structured Output Learning: Back to the Roots,
SCIA13(162-171).
Springer DOI 1311
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.
Elsevier DOI 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.
Elsevier DOI 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. 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.
IEEE DOI 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.
Elsevier DOI 0604
Unsupervised 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.
Elsevier DOI 0611
BibRef
And:
Dynamic local search for clustering with unknown number of clusters,
ICPR02(II: 240-243).
IEEE DOI 0211
Clustering; Gaussian mixture model; Single-pass; Large data sets 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.
Elsevier DOI 0711
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 0806
maximizes a generalization of the Wilcoxon-Mann-Whitney statistic on the training data BibRef

Srinivasan, B.V.[Balaji Vasan], Duraiswami, R.[Ramani],
Efficient subset selection via the kernelized Rényi distance,
ICCV09(1081-1088).
IEEE DOI 0909
BibRef

Hochbaum, D.S.[Dorit S.],
Polynomial Time Algorithms for Ratio Regions and a Variant of Normalized Cut,
PAMI(32), No. 5, May 2010, pp. 889-898.
IEEE DOI 1003
For clustering group similar objects, each group is dissimilar for others. BibRef

He, Z.S.[Zhao-Shui], Cichocki, A.[Andrzej], Xie, S.L.[Sheng-Li], Choi, K.[Kyuwan],
Detecting the Number of Clusters in n-Way Probabilistic Clustering,
PAMI(32), No. 11, November 2010, pp. 2006-2021.
IEEE DOI 1011
BibRef

Tan, S.C.[Swee Chuan], Ting, K.M.[Kai Ming], Teng, S.W.[Shyh Wei],
A general stochastic clustering method for automatic cluster discovery,
PR(44), No. 10-11, October-November 2011, pp. 2786-2799.
Elsevier DOI 1101
Clustering; Ant-based clustering; Automatic cluster detection BibRef

Dagher, I., Dahdah, K.,
Adaptive bandwidth mode detection algorithm,
IET-IPR(5), No. 8, 2011, pp. 645-660.
DOI Link 1108
recover the correct density function. BibRef

Qian, Q.A.[Qi-Ang], Chen, S.C.[Song-Can], Cai, W.L.[Wei-Ling],
Simultaneous clustering and classification over cluster structure representation,
PR(45), No. 6, June 2012, pp. 2227-2236.
Elsevier DOI 1202
Structure in data; Clustering learning; Classification learning; Simultaneous classification and clustering learning BibRef

Koonsanit, K.[Kitti], Jaruskulchai, C.[Chuleerat],
Automatic Determination of the Appropriate Number of Clusters for Multispectral Image Data,
IEICE(E95-D), No. 5, May 2012, pp. 1256-1263.
WWW Link. 1202
BibRef

Hansen, P.[Pierre], Ruiz, M.[Manuel], Aloise, D.[Daniel],
A VNS heuristic for escaping local extrema entrapment in normalized cut clustering,
PR(45), No. 12, December 2012, pp. 4337-4345.
Elsevier DOI 1208
Normalized cut; Clustering; Variable neighborhood search; Heuristics BibRef

Cheung, Y.M.[Yiu-Ming], Jia, H.[Hong],
Categorical-and-numerical-attribute data clustering based on a unified similarity metric without knowing cluster number,
PR(46), No. 8, August 2013, pp. 2228-2238.
Elsevier DOI 1304
Clustering; Similarity metric; Categorical attribute; Numerical attribute; Number of clusters BibRef

Liu, C.[Cong], Zhou, A.[Aimin], Zhang, G.X.[Gui-Xu],
Automatic clustering method based on evolutionary optimisation,
IET-CV(7), No. 4, 2013, pp. 258-271.
DOI Link 1307
Set the number of clusters. BibRef

Liu, C.[Cong], Zhou, A.[Aimin], Zhang, Q., Zhang, G.X.[Gui-Xu],
Adaptive image segmentation by using mean-shift and evolutionary optimisation,
IET-IPR(8), No. 6, June 2014, pp. 327-333.
DOI Link 1407
BibRef

Long, D.[Di], Singh, V.P.,
An Entropy-Based Multispectral Image Classification Algorithm,
GeoRS(51), No. 12, 2013, pp. 5225-5238.
IEEE DOI 1312
entropy BibRef

Chi, Y.J.[Yue-Jie], Porikli, F.M.[Fatih M.],
Classification and Boosting with Multiple Collaborative Representations,
PAMI(36), No. 8, August 2014, pp. 1519-1531.
IEEE DOI 1407
BibRef
Earlier:
Connecting the dots in multi-class classification: From nearest subspace to collaborative representation,
CVPR12(3602-3609).
IEEE DOI 1208
Biomedical measurement BibRef

Kolesnikov, A.[Alexander], Trichina, E.[Elena], Kauranne, T.[Tuomo],
Estimating the number of clusters in a numerical data set via quantization error modeling,
PR(48), No. 3, 2015, pp. 941-952.
Elsevier DOI 1412
Clustering BibRef

Hennig, C.[Christian],
What are the true clusters?,
PRL(64), No. 1, 2015, pp. 53-62.
Elsevier DOI 1509
Constructivism BibRef

Fornells, A.[Albert], Rodrigo, Z.[Zaida], Rovira, X.[Xari], Sánchez, M.[Mónica], Santomà, R.[Ricard], Teixidó-Navarro, F.[Francesc], Golobardes, E.[Elisabet],
Promoting consensus in the concept mapping methodology: An application in the hospitality sector,
PRL(67, Part 1), No. 1, 2015, pp. 39-48.
Elsevier DOI 1511
Concept mapping methodology BibRef

Liang, Z.[Zhou], Chen, P.[Pei],
Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering,
PRL(73), No. 1, 2016, pp. 52-59.
Elsevier DOI 1604
Divide-and-conquer BibRef

Li, H.P.[Hua-Peng], Zhang, S.Q.[Shu-Qing], Ding, X.H.[Xiao-Hui], Zhang, C.[Ce], Dale, P.[Patricia],
Performance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets,
RS(8), No. 4, 2016, pp. 295.
DOI Link 1604
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Saki, F.[Fatemeh], Kehtarnavaz, N.[Nasser],
Online frame-based clustering with unknown number of clusters,
PR(57), No. 1, 2016, pp. 70-83.
Elsevier DOI 1605
Online clustering for streaming data BibRef

Luo, J.J.[Juan-Juan], Jiao, L.C.[Li-Cheng], Shang, R.H.[Rong-Hua], Liu, F.[Fang],
Learning simultaneous adaptive clustering and classification via MOEA,
PR(60), No. 1, 2016, pp. 37-50.
Elsevier DOI 1609
Multiobjective optimization BibRef

Zhu, Y.[Ye], Ting, K.M.[Kai Ming], Carman, M.J.[Mark J.],
Density-ratio based clustering for discovering clusters with varying densities,
PR(60), No. 1, 2016, pp. 983-997.
Elsevier DOI 1609
Density-ratio BibRef

Carton, C.[Cérès], Lemaitre, A.[Aurélie], Coüasnon, B.[Bertrand],
Eyes Wide Open: An interactive learning method for the design of rule-based systems,
IJDAR(20), No. 2, June 2017, pp. 91-103.
Springer DOI 1706
rule-based document recognition systems. User guided analysis of document corpus. BibRef

Zhan, Q.M.[Qing-Ming], Deng, S.G.[Shu-Guang], Zheng, Z.H.[Zhi-Hua],
An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt,
IJGI(6), No. 9, 2017, pp. xx-yy.
DOI Link 1710
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Hayes, J.J.[James J.], Castillo, O.[Oscar],
A New Approach for Interpreting the Morisita Index of Aggregation through Quadrat Size,
IJGI(6), No. 10, 2017, pp. xx-yy.
DOI Link 1710
Analysis of spatial distribution of organisms. BibRef

Rudd, E.M.[Ethan M.], Jain, L.P.[Lalit P.], Scheirer, W.J.[Walter J.], Boult, T.E.[Terrance E.],
The Extreme Value Machine,
PAMI(40), No. 3, March 2018, pp. 762-768.
IEEE DOI 1802
Recognize that test inputs are not in a trained class. Bandwidth, Calibration, Extraterrestrial measurements, Kernel, Pattern recognition, Training, Visualization, Machine learning, supervised classification BibRef

Laber, E.S.[Eduardo Sany], de A. Mello Pereira, F.[Felipe],
Splitting criteria for classification problems with multi-valued attributes and large number of classes,
PRL(111), 2018, pp. 58-63.
Elsevier DOI 1808
Decision trees, Approximation algorithms, Attribute selection, Max-cut problem BibRef

Gialampoukidis, I.[Ilias], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis], Antoniou, I.[Ioannis],
Probabilistic density-based estimation of the number of clusters using the DBSCAN-martingale process,
PRL(123), 2019, pp. 23-30.
Elsevier DOI 1906
Density-based clustering, DBSCAN-Martingale, Graph-clustering, Image clustering, Number of clusters, Text clustering BibRef

Bhowmik, M.K.[Mrinal Kanti], Debnath, T.[Tathagata], Bhattacharjee, D.[Debotosh], Dutta, P.[Paramartha],
EF-Index: Determining number of clusters (K) to estimate number of segments (S) in an image,
IVC(88), 2019, pp. 29-40.
Elsevier DOI 1908
Clustering, Cluster-indexing, Segmentation, Electrostatic Force Image (EF-Image), Force Influence Image, BibRef

Song, C.[Ci], Pei, T.[Tao],
Decomposition of Repulsive Clusters in Complex Point Processes with Heterogeneous Components,
IJGI(8), No. 8, 2019, pp. xx-yy.
DOI Link 1909
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Shi, Z.C.[Zhi-Cheng], Ma, D.[Ding], Yan, X.[Xue], Zhu, W.[Wei], Zhao, Z.G.[Zhi-Gang],
A Density-Peak-Based Clustering Method for Multiple Densities Dataset,
IJGI(10), No. 9, 2021, pp. xx-yy.
DOI Link 2109
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Tabak, E.G.[Esteban G.], Trigila, G.[Giulio], Zhao, W.J.[Wen-Jun],
Distributional barycenter problem through data-driven flows,
PR(130), 2022, pp. 108795.
Elsevier DOI 2206
Optimal transport, Barycenter problem, Pattern visualization, Simulation, Generative models BibRef

Bagirov, A.M.[Adil M.], Aliguliyev, R.M.[Ramiz M.], Sultanova, N.[Nargiz],
Finding compact and well-separated clusters: Clustering using silhouette coefficients,
PR(135), 2023, pp. 109144.
Elsevier DOI 2212
Cluster analysis, Cluster validity index, Silhouette coefficients, Nonsmooth optimization, Incremental algorithm BibRef

Chen, Y.[Yinong], Debnath, T.[Tathagata], Cai, A.[Andrew], Song, M.Z.[Ming-Zhou],
Circular Silhouette and a Fast Algorithm,
PAMI(45), No. 11, November 2023, pp. 14038-14044.
IEEE DOI 2310
BibRef

Jazayeri, A.[Ali], Yang, C.C.[Christopher C.],
Frequent Pattern Mining in Continuous-Time Temporal Networks,
PAMI(46), No. 1, January 2024, pp. 305-321.
IEEE DOI 2312
Continuous-time networks, frequent subgraphs, pattern mining, temporal networks BibRef

Zhao, B.X.[Bo-Xiang], Wang, S.L.[Shu-Liang], Chi, L.H.[Lian-Hua], Yuan, H.[Hanning], Yuan, Y.[Ye], Li, Q.[Qi], Geng, J.[Jing], Zhang, S.L.[Shao-Liang],
Coresets for fast causal discovery with the additive noise model,
PR(148), 2024, pp. 110149.
Elsevier DOI 2402
Causal discovery, Coresets, Functional causal model, Additive noise model, Big data BibRef

Serrano, B.[Breno], Vidal, T.[Thibaut],
Community detection in the stochastic block model by mixed integer programming,
PR(152), 2024, pp. 110487.
Elsevier DOI 2405
Community detection, Stochastic block model, Mixed integer programming, Machine learning, Local search BibRef

Hu, Z.X.[Zhan-Xuan], Xu, Y.[Yan], He, L.[Lang], Nie, F.P.[Fei-Ping],
Interactive Supervision for New Intent Discovery,
SPLetters(31), 2024, pp. 1680-1684.
IEEE DOI 2407
Representation learning, Semantics, Measurement, Banking, Training, Reliability, Vectors, Intent discovery, clustering, embedding BibRef

Chi, H.A.[Hao-Ang], Yang, W.J.[Wen-Jing], Liu, F.[Feng], Lan, L.[Long], Qin, T.[Tao], Han, B.[Bo],
Does Confusion Really Hurt Novel Class Discovery?,
IJCV(132), No. 8, August 2024, pp. 3191-3207.
Springer DOI 2408
BibRef

Hou, J.[Jian], Lin, H.[Houshen], Yuan, H.Q.[Hua-Qiang], Pelillo, M.[Marcello],
Flexible density peak clustering for real-world data,
PR(156), 2024, pp. 110772.
Elsevier DOI 2408
Clustering, Density peak, Real-world data, Number of clusters BibRef

Li, Y.X.[Yong-Xiang], Qin, Y.[Yang], Sun, Y.[Yuan], Peng, D.Z.[De-Zhong], Peng, X.[Xi], Hu, P.[Peng],
RoMo: Robust Unsupervised Multimodal Learning With Noisy Pseudo Labels,
IP(33), 2024, pp. 5086-5097.
IEEE DOI 2410
Noise measurement, Semantics, Noise, Predictive models, Feature extraction, Annotations, robust discriminant learning BibRef

Zeng, P.X.[Peng-Xin], Li, Y.F.[Yun-Fan], Hu, P.[Peng], Peng, D.Z.[De-Zhong], Lv, J.C.[Jian-Cheng], Peng, X.[Xi],
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric,
CVPR23(23986-23995)
IEEE DOI 2309

WWW Link. BibRef

Otholt, J.[Jona], Meinel, C.[Christoph], Yang, H.J.[Hao-Jin],
Guided Cluster Aggregation: A Hierarchical Approach to Generalized Category Discovery,
WACV24(2606-2615)
IEEE DOI Code:
WWW Link. 2404
Image recognition, Codes, Aggregates, Clustering algorithms, Machine learning, Semisupervised learning, Algorithms, Image recognition and understanding BibRef

Wen, X.[Xin], Zhao, B.C.[Bing-Chen], Qi, X.J.[Xiao-Juan],
Parametric Classification for Generalized Category Discovery: A Baseline Study,
ICCV23(16544-16554)
IEEE DOI Code:
WWW Link. 2401
BibRef

Du, R.Y.[Ruo-Yi], Chang, D.L.[Dong-Liang], Liang, K.M.[Kong-Ming], Hospedales, T.M.[Timothy M.], Song, Y.Z.[Yi-Zhe], Ma, Z.Y.[Zhan-Yu],
On-the-Fly Category Discovery,
CVPR23(11691-11700)
IEEE DOI 2309

WWW Link. BibRef

Li, W.B.[Wen-Bin], Fan, Z.C.[Zhi-Chen], Huo, J.[Jing], Gao, Y.[Yang],
Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery,
CVPR23(3449-3458)
IEEE DOI 2309
BibRef

Yang, M.[Muli], Wang, L.[Liancheng], Deng, C.[Cheng], Zhang, H.W.[Han-Wang],
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery,
CVPR23(3459-3468)
IEEE DOI 2309
BibRef

Zhang, S.[Sheng], Khan, S.[Salman], Shen, Z.Q.[Zhi-Qiang], Naseer, M.[Muzammal], Chen, G.Y.[Guang-Yi], Khan, F.S.[Fahad Shahbaz],
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery,
CVPR23(3479-3488)
IEEE DOI 2309
BibRef

Sohn, K.[Kihyuk], Yoon, J.[Jinsung], Li, C.L.[Chun-Liang], Lee, C.Y.[Chen-Yu], Pfister, T.[Tomas],
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types,
WACV23(5468-5479)
IEEE DOI 2302
Clustering methods, Euclidean distance, Mutual information, Anomaly detection BibRef

Joseph, K.J., Paul, S.[Sujoy], Aggarwal, G.[Gaurav], Biswas, S.[Soma], Rai, P.[Piyush], Han, K.[Kai], Balasubramanian, V.N.[Vineeth N.],
Spacing Loss for Discovering Novel Categories,
CLVision22(3760-3765)
IEEE DOI 2210
Training, Computational modeling, Machine learning, Benchmark testing, Data models BibRef

Gialampoukidis, I.[Ilias], Andreadis, S.[Stelios], Pantelidis, N.[Nick], Hayat, S.[Sameed], Zhong, L.[Li], Bakratsas, M.[Marios], Hoppe, D.[Dennis], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis],
Parallel DBSCAN-Martingale Estimation of the Number of Concepts for Automatic Satellite Image Clustering,
MMMod22(I:95-106).
Springer DOI 2203
BibRef

Fini, E.[Enrico], Sangineto, E.[Enver], Lathuilière, S.[Stéphane], Zhong, Z.[Zhun], Nabi, M.[Moin], Ricci, E.[Elisa],
A Unified Objective for Novel Class Discovery,
ICCV21(9264-9272)
IEEE DOI 2203
Interference, Benchmark testing, Linear programming, Feature extraction, Unsupervised learning, Recognition and classification BibRef

Kamgar-Parsi, B.[Behzad], Kamgar-Parsi, B.[Behrooz],
Penalized K-Means Algorithms for Finding the Number of Clusters,
ICPR21(969-974)
IEEE DOI 2105
Additives, Clustering algorithms, Reliability, Guidelines, additive penalty, cludtering, multiplicative penalty, unsupervised learning BibRef

Yang, L.[Lei], Zhan, X.H.[Xiao-Hang], Chen, D.P.[Da-Peng], Yan, J.J.[Jun-Jie], Loy, C.C.[Chen Change], Lin, D.[Dahua],
Learning to Cluster Faces on an Affinity Graph,
CVPR19(2293-2301).
IEEE DOI 2002
Clustering unlabeled faces. BibRef

Bondarev, A.E.,
Visual Analysis And Processing of Clusters Structures In Multidimensional Datasets,
PTVSBB17(151-154).
DOI Link 1805
BibRef

Wang, Z.Y.[Zi-Yin], Tsechpenakis, G.[Gavriil],
Feature Clustering with Fading Affect Bias: Building Visual Vocabularies on the Fly,
CIAP17(I:445-456).
Springer DOI 1711
BibRef

Levinkov, E.[Evgeny], Kirillov, A.[Alexander], Andres, B.[Bjoern],
A Comparative Study of Local Search Algorithms for Correlation Clustering,
GCPR17(103-114).
Springer DOI 1711
Applied to image segmentation, hand-written digit classification and social network analysis. BibRef

Liu, J.C.[Jun-Cheng], Lian, Z.H.[Zhou-Hui], Wang, Y.[Yi], Xiao, J.G.[Jian-Guo],
Incremental Kernel Null Space Discriminant Analysis for Novelty Detection,
CVPR17(4123-4131)
IEEE DOI 1711
Is the data really part of any current class. Algorithm design and analysis, Null space, Pattern recognition, Training, Training, data BibRef

Guo, P.C.[Peng-Cheng], Wang, X.[Xing], Wang, Y.B.[Yu-Bing], Cheng, Y.[Yue], Zhang, Y.[Ying],
Research on automatic determining clustering centers algorithm based on linear regression analysis,
ICIVC17(1016-1023)
IEEE DOI 1708
clustering center, density peak, linear regression, residual, analysis BibRef

Comiter, M.[Marcus], Cha, M.[Miriam], Kung, H.T., Teerapittayanon, S.[Surat],
Lambda means clustering: Automatic parameter search and distributed computing implementation,
ICPR16(2331-2337)
IEEE DOI 1705
Clustering algorithms, Computers, Distributed computing, Elbow, Measurement, Multicore processing, Partitioning, algorithms BibRef

Wang, Y., Li, Y., Zhao, Q.H.,
Coupling Regular Tessellation with RJMCMC Algorithm to Segment SAR Image With Unknown Number Of Classes,
ISPRS16(B7: 393-397).
DOI Link 1610
BibRef

Zhao, X.M.[Xue-Mei], Li, Y.[Yu], Zhao, Q.H.[Quan-Hua], Wang, C.Y.[Chun-Yan],
An Entropy-kl Strategy For Estimating Number Of Classes In Image Segmentation Issues,
ISPRS16(B7: 437-441).
DOI Link 1610
BibRef

Zhang, Z.M.[Zi-Ming], Chen, Y.T.[Yu-Ting], Saligrama, V.[Venkatesh],
Group Membership Prediction,
ICCV15(3916-3924)
IEEE DOI 1602
Predict whether a collection of instances share a certain semantic property. BibRef

Tepper, M.[Mariano], Sapiro, G.[Guillermo],
From Local to Global Communities in Large Networks Through Consensus,
CIARP15(659-666).
Springer DOI 1511
BibRef

Mohammed, A.J.[Athraa Jasim], Yusof, Y.[Yuhanis], Husni, H.[Husniza],
Determining Number of Clusters Using Firefly Algorithm with Cluster Merging for Text Clustering,
IVIC15(14-24).
Springer DOI 1511
BibRef

Kim, M.[Minkyu], Lim, J.M.[Jeong-Mook], Shin, H.[Heesook], Oh, C.[Changmok], Jeong, H.T.[Hyun-Tae],
Estimating the Number of Clusters with Database for Texture Segmentation Using Gabor Filter,
CVS15(435-444).
Springer DOI 1507
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Hautamäki, V.[Ville], Pöllänen, A.[Antti], Kinnunen, T.[Tomi], Lee, K.A.[Kong Aik], Li, H.Z.[Hai-Zhou], Fränti, P.[Pasi],
A Comparison of Categorical Attribute Data Clustering Methods,
SSSPR14(53-62).
Springer DOI 1408
BibRef

Kading, C.[Christoph], Freytag, A.[Alexander], Rodner, E.[Erik], Bodesheim, P.[Paul], Denzler, J.[Joachim],
Active learning and discovery of object categories in the presence of unnameable instances,
CVPR15(4343-4352)
IEEE DOI 1510
BibRef

Al-Rawi, M.S.[Mohammed Sadeq], Cunha, J.P.S.[João Paulo Silva],
Using Permutation Tests to Study How the Dimensionality, the Number of Classes, and the Number of Samples Affect Classification Analysis,
ICIAR12(I: 34-42).
Springer DOI 1206
BibRef

Kolesnikov, A.[Alexander], Trichina, E.[Elena],
Determining the Number of Clusters with Rate-Distortion Curve Modeling,
ICIAR12(I: 43-50).
Springer DOI 1206
BibRef

Mittal, M.[Mamta], Singh, V.P., Sharma, R.K.,
Random automatic detection of clusters,
ICIIP11(1-6).
IEEE DOI 1112
BibRef

Chen, G.L.[Guang-Liang], Maggioni, M.[Mauro],
Multiscale geometric and spectral analysis of plane arrangements,
CVPR11(2825-2832).
IEEE DOI 1106
Based on SVD clustering. BibRef

Gopalan, R.[Raghuraman], Sankaranarayanan, J.[Jagan],
Max-margin clustering: Detecting margins from projections of points on lines,
CVPR11(2769-2776).
IEEE DOI 1106
BibRef

Zeng, Z.M.[Zi-Ming], Wang, W.H.[Wen-Hui], Yang, L.Z.[Long-Zhi], Zwiggelaar, R.[Reyer],
Automatic Estimation of the Number of Segmentation Groups Based on MI,
IbPRIA11(532-539).
Springer DOI 1106
Mutual Information BibRef

Thakoor, N.[Ninad], Devarajan, V.[Venkat], Gao, J.X.[Jean X.],
Computation complexity of branch-and-bound model selection,
ICCV09(1895-1900).
IEEE DOI 0909
Segmentation. Number of clusters.
See also Multistage Branch-and-Bound Merging for Planar Surface Segmentation in Disparity Space. 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 0812
BibRef

Li, F.J.[Fa-Jie], Klette, R.[Reinhard],
Recovery Rate of Clustering Algorithms,
PSIVT09(1058-1069).
Springer DOI 0901
Given old clusters, evaluation of performance to compute new clusters.
See also Decomposing a Simple Polygon into Trapezoids. BibRef

Franti, P.[Pasi], Virmajoki, O.[Olli], Hautamaki, V.[Ville],
Probabilistic clustering by random swap algorithm,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Zhao, Q.P.[Qin-Pei], Hautamaki, V.[Ville], Fränti, P.[Pasi],
Knee Point Detection in BIC for Detecting the Number of Clusters,
ACIVS08(xx-yy).
Springer DOI 0810
BibRef

Zhang, Z.M.[Zi-Ming], Chan, S.[Syin], Chia, L.T.[Liang-Tien],
Discriminative Signatures for Image Classification,
ICIP07(II: 197-200).
IEEE DOI 0709
Discover discriminable features for classification. BibRef

Grim, J.[Jirí],
EM Cluster Analysis for Categorical Data,
SSPR06(640-648).
Springer DOI 0608
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 0608
A 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).
IEEE DOI 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 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 0505
BibRef

Law, M.H.C.[Martin H.C.], Topchy, A.P.[Alexander P.], Jain, A.K.,
Multiobjective data clustering,
CVPR04(II: 424-430).
IEEE DOI 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 DOI 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 BibRef 9600

Wallace, R.S., and Kanade, T.,
Finding Natural Clusters Having Minimal Description Lengths,
ICPR90(I: 438-442).
IEEE DOI BibRef 9000

Bandapadhay, A., Fu, J.L.,
Searching parameter spaces with noisy linear constraints,
CVPR88(550-555).
IEEE DOI 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
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