14.2.2.1 Density Based Clustering

Chapter Contents (Back)
Clustering. Density Based.

Deng, Z.H.[Zhao-Hong], Chung, F.L.[Fu-Lai], Wang, S.T.[Shi-Tong],
FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation,
PR(41), No. 4, April 2008, pp. 1363-1372.
Elsevier DOI 0801
Reduced set density estimator; Minimal enclosing ball; Core-set; Data condensation BibRef

Ozertem, U.[Umut], Erdogmus, D.[Deniz], Jenssen, R.[Robert],
Mean shift spectral clustering,
PR(41), No. 6, June 2008, pp. 1924-1938.
Elsevier DOI 0802
Similarity based clustering; Nonparametric density estimation; Mean shift; Connected components; Spectral clustering BibRef

Zhang, X.C.[Xian-Chao], Li, J.W.[Jing-Wei], Yu, H.[Hong],
Local density adaptive similarity measurement for spectral clustering,
PRL(32), No. 2, 15 January 2011, pp. 352-358.
Elsevier DOI 1101
Clustering; Spectral clustering; Similarity measure BibRef

Ye, Y.D.[Yang-Dong], Ren, Y.L.[Yong-Li], Li, G.[Gang],
Using local density information to improve IB algorithms,
PRL(32), No. 2, 15 January 2011, pp. 310-320.
Elsevier DOI 1101
Information Bottleneck; Density; Neighborhood information; Hierarchical tree-structure BibRef

Fu, G.[Gang], Shih, F.Y.[Frank Y.], Wang, H.M.[Hai-Min],
A kernel-based parametric method for conditional density estimation,
PR(44), No. 2, February 2011, pp. 284-294.
Elsevier DOI 1011
Conditional density estimation; Kernel principal component analysis; Kernel function; Nadaraya-Watson estimator BibRef

Leiva-Murillo, J.M.[José M.], Artés-Rodríguez, A.[Antonio],
Algorithms for maximum-likelihood bandwidth selection in kernel density estimators,
PRL(33), No. 13, 1 October 2012, pp. 1717-1724.
Elsevier DOI 1208
Kernel density estimation; Multivariate density modeling; Pattern recognition BibRef

Tu, Q., Lu, J.F., Yuan, B., Tang, J.B., Yang, J.Y.,
Density-based hierarchical clustering for streaming data,
PRL(33), No. 5, 1 April 2012, pp. 641-645.
Elsevier DOI 1202
Streaming data; Density-based clustering; Hierarchical method BibRef

Sarmah, S.[Sauravjyoti], Bhattacharyya, D.K.[Dhruba K.],
A grid-density based technique for finding clusters in satellite image,
PRL(33), No. 5, 1 April 2012, pp. 589-604.
Elsevier DOI 1202
Clustering; Grid; Density; High resolution; High dimensional satellite images; Gabor wavelets. Clusters are very different sizes. BibRef

Mountrakis, G.[Giorgos], Xi, B.[Bo],
Assessing reference dataset representativeness through confidence metrics based on information density,
PandRS(78), No. 1, April 2013, pp. 129-147.
Elsevier DOI 1304
Representativeness; Classification; Quality; Reference data; Reliability; Training BibRef

Parker, J.K.[Jonathon K.], Downs, J.A.[Joni A.],
Footprint generation using fuzzy-neighborhood clustering,
GeoInfo(17), No. 2, April 2013, pp. 285-299.
Springer DOI 1304
Expand on Density-Based Spatial Clustering with Noise (DBSCAN). BibRef

Hou, J.[Jian], Pelillo, M.[Marcello],
A simple feature combination method based on dominant sets,
PR(46), No. 11, November 2013, pp. 3129-3139.
Elsevier DOI 1306
Feature combination; Object classification; Dominant sets; Kernel accuracy BibRef

Hou, J.[Jian], Cui, H.X.[Hong-Xia],
Density Normalization in Density Peak Based Clustering,
GbRPR17(187-196).
Springer DOI 1706
BibRef

Hou, J.[Jian], Pelillo, M.[Marcello],
A new density kernel in density peak based clustering,
ICPR16(468-473)
IEEE DOI 1705
Clustering algorithms, Correlation, Data models, Density measurement, Estimation, Kernel, Shape BibRef

Lourenço, A.[André], Bulň, S.R.[Samuel Rota], Rebagliati, N.[Nicola], Fred, A.[Ana], Figueiredo, M.A.T.[Mário A.T.],
Consensus Clustering Using Partial Evidence Accumulation,
IbPRIA13(69-78).
Springer DOI 1307
BibRef

Inkaya, T.[Tülin], Kayaligil, S.[Sinan], Özdemirel, N.E.[Nur Evin],
An adaptive neighbourhood construction algorithm based on density and connectivity,
PRL(52), No. 1, 2015, pp. 17-24.
Elsevier DOI 1412
Data neighbourhood BibRef

Kumar, K.M.[K. Mahesh], Reddy, A.R.M.[A. Rama Mohan],
A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method,
PR(58), No. 1, 2016, pp. 39-48.
Elsevier DOI 1606
Density Based Spatial Clustering of Applications with Noise. Unsupervised learning BibRef

Du, Q.Y.[Qing-Yun], Dong, Z.[Zhi], Huang, C.D.[Chu-Dong], Ren, F.[Fu],
Density-Based Clustering with Geographical Background Constraints Using a Semantic Expression Model,
IJGI(5), No. 5, 2016, pp. 72.
DOI Link 1606
four types of constraints for geographical backgrounds: No Constraints, Constraints, Cannot-Link Constraints, and Must-Link Constraints. BibRef

Louhichi, S.[Soumaya], Gzara, M.[Mariem], Ben-Abdallah, H.[Hanęne],
Unsupervised varied density based clustering algorithm using spline,
PRL(93), No. 1, 2017, pp. 48-57.
Elsevier DOI 1706
Data, mining BibRef

Deutsch, L.[Lior], Horn, D.[David],
The Weight-Shape decomposition of density estimates: A framework for clustering and image analysis algorithms,
PR(81), 2018, pp. 190-199.
Elsevier DOI 1806
Density estimate, Quantum clustering, Mean-shift clustering, Maximum entropy, Image contour extraction BibRef

Tu, D.[Ding], Chen, L.[Ling], Yu, X.K.[Xiao-Kang], Chen, G.C.[Gen-Cai],
Semisupervised Prior Free Rare Category Detection With Mixed Criteria,
Cyber(48), No. 1, January 2018, pp. 115-126.
IEEE DOI 1801
Clustering methods, Data models, Estimation, Kernel, Measurement, Semisupervised learning, Density-based clustering, semisupervised learning BibRef

Liu, A.[Anjin], Lu, J.[Jie], Liu, F.[Feng], Zhang, G.Q.[Guang-Quan],
Accumulating regional density dissimilarity for concept drift detection in data streams,
PR(76), No. 1, 2018, pp. 256-272.
Elsevier DOI 1801
Concept drift BibRef

Wang, T.F.[Tian-Fu], Ren, C.[Chang], Luo, Y.[Yun], Tian, J.[Jing],
NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space,
IJGI(8), No. 5, 2019, pp. xx-yy.
DOI Link 1906
BibRef

Alshammari, M.[Mashaan], Takatsuka, M.[Masahiro],
Approximate spectral clustering density-based similarity for noisy datasets,
PRL(128), 2019, pp. 155-161.
Elsevier DOI 1912
BibRef

Wells, J.R.[Jonathan R.], Ting, K.M.[Kai Ming],
A new simple and efficient density estimator that enables fast systematic search,
PRL(122), 2019, pp. 92-98.
Elsevier DOI 1904
Density estimation, Histogram, Outlying aspect mining BibRef

Cervellera, C.[Cristiano], Macciň, D.[Danilo],
Voronoi tree models for distribution-preserving sampling and generation,
PR(97), 2020, pp. 107002.
Elsevier DOI 1910
Voronoi tree models, Sampling, Generative models, Density estimation, Noparametric models BibRef

Li, H.[Hao], Liu, X.[Xiaojie], Li, T.[Tao], Gan, R.[Rundong],
A novel density-based clustering algorithm using nearest neighbor graph,
PR(102), 2020, pp. 107206.
Elsevier DOI 2003
Density-based clustering, Nearest neighbor graph, DBSCAN BibRef


Ghosh, S.[Soumyadeep], Singh, R.[Richa], Vatsa, M.[Mayank],
On Learning Density Aware Embeddings,
CVPR19(4879-4887).
IEEE DOI 2002
BibRef

Xu, S., Dai, J., shi, H.,
Semi-supervised Feature Selection by Mutual Information Based on Kernel Density Estimation,
ICPR18(818-823)
IEEE DOI 1812
Feature extraction, Mutual information, Kernel, Entropy, Estimation, Probability density function, Bandwidth BibRef

Chang, S.R.[Shao-Rong], Dasgupta, N.[Nilanjan], Carin, L.[Lawrence],
A Bayesian Approach to Unsupervised Feature Selection and Density Estimation Using Expectation Propagation,
CVPR05(II: 1043-1050).
IEEE DOI 0507
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Binary Clustering, Two Class Classification .


Last update:Jun 29, 2020 at 10:24:28