Geodesic
A hybrid clustering algorithm based on PSO with dynamic crossover
Nečetkie sistemy i mâgkie vyčisleniâ, Tome 12 (2017) no. 2, pp. 87-96.

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

In this paper an approach to clustering of short text fragments by hierarchical classifier is proposed. This approach is based on a special way of object representation in the dimension of features constructed by hierarchical classifier. Also, in order to reasonable perform quality evaluation of clustering results there is a set of criteria proposed in this paper. Presented experimental results show the ability of this approach to improve significantly the quality of clustering.
Keywords: clustering, fuzzy classifier, natural language processing, evaluation of clustering. 
@article{FSSC_2017_12_2_a0,
     author = {P. V. Dudarin and N. G. Yarushkina},
     title = {A hybrid clustering algorithm based on {PSO} with dynamic crossover},
     journal = {Ne\v{c}etkie sistemy i m\^agkie vy\v{c}isleni\^a},
     pages = {87--96},
     publisher = {mathdoc},
     volume = {12},
     number = {2},
     year = {2017},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/FSSC_2017_12_2_a0/}
}
TY  - JOUR
AU  - P. V. Dudarin
AU  - N. G. Yarushkina
TI  - A hybrid clustering algorithm based on PSO with dynamic crossover
JO  - Nečetkie sistemy i mâgkie vyčisleniâ
PY  - 2017
SP  - 87
EP  - 96
VL  - 12
IS  - 2
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/FSSC_2017_12_2_a0/
LA  - ru
ID  - FSSC_2017_12_2_a0
ER  - 
%0 Journal Article
%A P. V. Dudarin
%A N. G. Yarushkina
%T A hybrid clustering algorithm based on PSO with dynamic crossover
%J Nečetkie sistemy i mâgkie vyčisleniâ
%D 2017
%P 87-96
%V 12
%N 2
%I mathdoc
%U http://geodesic.mathdoc.fr/item/FSSC_2017_12_2_a0/
%G ru
%F FSSC_2017_12_2_a0
P. V. Dudarin; N. G. Yarushkina. A hybrid clustering algorithm based on PSO with dynamic crossover. Nečetkie sistemy i mâgkie vyčisleniâ, Tome 12 (2017) no. 2, pp. 87-96. http://geodesic.mathdoc.fr/item/FSSC_2017_12_2_a0/

[1] Dudarin P. V., Jarushkina N. G., “Algorithm for constructing a hierarchical classifier of short text fragments based on the clustering of a fuzzy graph”, Radio Engineering, 2017, no. 6, 114-121 (in Russian)

[2] Sibirev I. V., “Indices of the evaluation of clustering results”, Fuzzy Systems, Soft Computing and Intellectual Technology NSMVIT-2017: Proceedings of the VII All-Russian Scientific and Practical Conference (Saint-Petersburg, 3-7 July 2017), v. 1, 129-138 (in Russian)

[3] Amigo E., Gonzalo J., Artiles J., Verdejo F., “A comparison of extrinsic clustering evaluation metrics based on formal constraints”, Information Retrieval, 12:4 (2009), 461-486 | DOI

[4] Ball G. H., Hall D. J., Isodata, a Novel Method of Data Analysis and Pattern Classification, Technical Report prepared for Information Sciences Branch, Office of Naval Research, Stanford Research Institute, Menlo Park, California, 1965

[5] Campello R. J. G. B., Moulavi D., Sander J., “Density-Based Clustering Based on Hierarchical Density Estimates”, Advances in Knowledge Discovery and Data Mining. PAKDD 2013, Lecture Notes in Computer Science, 7819, Springer, Berlin, Heidelberg, 2013, 160-172 | DOI

[6] Chen Y., Han M., Zhu H., “Ant spatial clustering based on fuzzy IF-THEN rule”, Fuzzy Information and Engineering 2010, Advances in Intelligent and Soft Computing, 78, Springer, Berlin, Heidelberg, 2010 | DOI

[7] Manning C. D., Raghavan P., Schütze H., An Introduction to Information Retrieval, Cambridge University Press, Cambridge, England, 2009

[8] Dudarin P. V., Yarushkina N. G., “An approach to fuzzy hierarchical clustering of short text fragments based on fuzzy graph clustering”, Proceedings of the Second International Scientific Conference «Intelligent Information Technologies for Industry» (IITI’17), IITI 2017, Advances in Intelligent Systems and Computing, 679, Springer, Cham, 2018, 295-304 | DOI

[9] Kapil S., Chawla M., Ansari M. D., “On K-means data clustering algorithm with genetic algorithm”, Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC) (Waknaghat, 2016), 202-206

[10] Mansoori E. G., “GACH: a grid based algorithm for hierarchical clustering of high-dimensional data”, Soft Computing, 18:5 (2014), 905-922 | DOI

[11] McInnes L., Healy J., Astels S., “hdbscan: Hierarchical density based clustering”, Journal of Open Source Software, 2:11 (2017) | DOI

[12] Zhang J., Wang Y., Feng J., “A hybrid clustering algorithm based on PSO with dynamic crossover”, Soft Computing, 18:5 (2014), 961-979 | DOI