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@article{JSFU_2018_11_3_a7,
author = {Michael G. Sadovsky and Anatoly N. Ostylovsky},
title = {New method to determine topology of low-dimension manifold approximating multidimensional data sets},
journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
pages = {322--328},
publisher = {mathdoc},
volume = {11},
number = {3},
year = {2018},
language = {en},
url = {http://geodesic.mathdoc.fr/item/JSFU_2018_11_3_a7/}
}
TY - JOUR AU - Michael G. Sadovsky AU - Anatoly N. Ostylovsky TI - New method to determine topology of low-dimension manifold approximating multidimensional data sets JO - Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika PY - 2018 SP - 322 EP - 328 VL - 11 IS - 3 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/JSFU_2018_11_3_a7/ LA - en ID - JSFU_2018_11_3_a7 ER -
%0 Journal Article %A Michael G. Sadovsky %A Anatoly N. Ostylovsky %T New method to determine topology of low-dimension manifold approximating multidimensional data sets %J Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika %D 2018 %P 322-328 %V 11 %N 3 %I mathdoc %U http://geodesic.mathdoc.fr/item/JSFU_2018_11_3_a7/ %G en %F JSFU_2018_11_3_a7
Michael G. Sadovsky; Anatoly N. Ostylovsky. New method to determine topology of low-dimension manifold approximating multidimensional data sets. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 11 (2018) no. 3, pp. 322-328. http://geodesic.mathdoc.fr/item/JSFU_2018_11_3_a7/
[1] A.N. Gorban, B.Kégl, D. IIIWünsch , A. Zinovyev (eds.), Principal Manifolds for Data Visualisation and Dimension Reduction, LNCSE, 58, Springer, Berlin–Heidelberg–New York, 2007 | MR
[2] A.N. Gorban, D. Roose (eds.), Coping with Complexity: Model Reduction and Data Analysis, Lecture Notes in Computational Science and Engineering, Springer, 2010 | MR
[3] A.N. Gorban, I. Yu. Tyukin, D.V. Prokhorov, K.I. Sofeikov, “Approximation with random bases: Pro et Contra”, Information Sciences, 364–365 (2016), 129–145 | DOI
[4] E.M. Mirkes , I. Alexandrakis, K. Slater, R. Tuli, A.N. Gorban, “Computational diagnosis and risk evaluation for canine lymphoma”, Computers in Biology and Medicine, 53 (2014), 279–290 | DOI
[5] J. Leskovec, A. Rajaraman, J.D. Ullman, Mining of massive datasets, Cambridge Univ. Press, 2014 | MR
[6] A. Fahad, Alshatri N., Z. Tari, A. Alamri, I. Khalil, A.Y. Zomaya, S. Foufou, A. Bouras, “A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis”, IEEE Trans. on emerging topics in computing, 2:3 (2014), 267–279 | DOI
[7] Dongkuan Xu, Yingjie Tian, “A Comprehensive Survey of Clustering Algorithms”, Ann. Data. Sci., 2:2 (2015), 165–193 | DOI
[8] M. Girvan, M.E.J. Newman, Community structure in social and biological networks, PNAS, 99:12 (2002), 7821–7826 | DOI | MR | Zbl
[9] A.A. Akinduko, E.M. Mirkes, A.N. Gorban, “SOM: Stochastic initialization versus principal components”, Information Sciences, 364–365 (2016), 213–221 | DOI
[10] M.G. Sadovsky, A.N. Ostylovsky, “New Clusterization Method Based on Graph Connectivity Search”, Journal of Siberian Federal University. Mathematics $\$ Physics, 10:4 (2017), 443–449 | DOI | MR
[11] D. Comaniciu, P. Meer, “Mean shift: A robust approach toward feature space analysis”, IEEE Trans. Pattern Anal. Machine Intell., 24 (2002), 603–619 | DOI
[12] O. Lecarme, K. Delvare, The Book of GIMP: A Complete Guide to Nearly Everything, No Starch Press, 2013
[13] M. Ester, H.-P. Kriegel, J. Sander, X. Xiaowei, “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, KDD-96 Proceedings, eds. E. Simoudis, J. Han, U. Fayyad, 1996, 226–231
[14] J. Sander, M. Ester, H.-P. Kriegel, X. Xiaowei, “Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications”, Data Mining and Knowledge Discovery, 2:2 (1998), 169–194 | DOI | MR