New clusterization method based on graph connectivity search
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 10 (2017) no. 4, pp. 443-449.

Voir la notice de l'article provenant de la source Math-Net.Ru

New method is proposed to identify clusters in datasets. The method is based on a sequential elimination of the longest distances in dataset, so that the relevant graph looses some edges. The method stops when the graph becomes disconnected.
Keywords: order, complexity, clusterization, component, connectivity.
@article{JSFU_2017_10_4_a4,
     author = {Michael G. Sadovsky and Eugene Yu. Bushmelev and Anatoly N. Ostylovsky},
     title = {New clusterization method based on graph connectivity search},
     journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
     pages = {443--449},
     publisher = {mathdoc},
     volume = {10},
     number = {4},
     year = {2017},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a4/}
}
TY  - JOUR
AU  - Michael G. Sadovsky
AU  - Eugene Yu. Bushmelev
AU  - Anatoly N. Ostylovsky
TI  - New clusterization method based on graph connectivity search
JO  - Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika
PY  - 2017
SP  - 443
EP  - 449
VL  - 10
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a4/
LA  - en
ID  - JSFU_2017_10_4_a4
ER  - 
%0 Journal Article
%A Michael G. Sadovsky
%A Eugene Yu. Bushmelev
%A Anatoly N. Ostylovsky
%T New clusterization method based on graph connectivity search
%J Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika
%D 2017
%P 443-449
%V 10
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a4/
%G en
%F JSFU_2017_10_4_a4
Michael G. Sadovsky; Eugene Yu. Bushmelev; Anatoly N. Ostylovsky. New clusterization method based on graph connectivity search. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 10 (2017) no. 4, pp. 443-449. http://geodesic.mathdoc.fr/item/JSFU_2017_10_4_a4/

[1] J. Leskovec, A. Rajaraman, J. DUllman, Mining of massive datasets, Cambridge Univ. Press, 2014 | MR

[2] A. Fahad, N. Alshatri, 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

[3] Dongkuan Xu, Yingjie Tian, “A Comprehensive Survey of Clustering Algorithms”, Ann. Data. Sci., 2:2 (2015), 165–193 | DOI

[4] M. Gavhale, P. D. Saraf, “Survey on Algorithms for Efficient Cluster Formation and Cluster Head Selection in MANET”, Procedia Computer Science, 78 (2016), 477–482 | DOI

[5] Ka-Chun Wong, A Short Survey on Data Clustering Algorithms, 2015, arXiv: 1511.09123v1 [cs.DS]

[6] R. Xu, D. Wunsch II, “Survey of Clustering Algorithms”, IEEE Trans. on neural networks, 16:3 (2005), 645–678 | DOI

[7] A. Jain, R. C. Dubes, Algorithms vor clustering data, Prentice-Hall, Inc., 1988, xiv pp. | MR

[8] J. A. Bondy, U. S. R. Murty, Graph theory with applications, Elsevier, 2007 | MR

[9] R. Diestel, Graph theory, Springer, 2000 | MR

[10] O. Ore, Theory of graphs, 3$^{\textrm{rd}}$ ed., AMS, 1962 | MR | Zbl

[11] G. W. Zobrist, J. V. Leonard, Progress in simulation, v. 2, Ablex Publ. Corp., New Jersey, 1994

[12] M. Girvan, M. E. J. Newman, “Community structure in social and biological networks”, PNAS, 99:12 (2002), 7821–7826 | DOI | MR | Zbl

[13] A. A. Akinduko, E. M. Mirkes, A. N. Gorban, “SOM: Stochastic initialization versus principal components”, Information Sciences, 364–365 (2016), 213–221 | DOI