{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T21:36:43Z","timestamp":1775857003152,"version":"3.50.1"},"reference-count":36,"publisher":"RTU MIREA","issue":"6","license":[{"start":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T00:00:00Z","timestamp":1578614400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.rtj-mirea.ru\/jour\/about\/editorialPolicies#openAccessPolicy"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Rossijskij tehnologi\u010deskij \u017eurnal"],"abstract":"<jats:p>The genetic algorithm of clustering of analysis objects in different data domains has been offered within the hybrid concept of intelligent information technologies development aimed to support decision-making. The algorithm makes it possible to account for different preferences of the analyst in clustering reflected in a calculation formula of fitness function. The place of this algorithm among those used for cluster analysis has been shown. The algorithm is simple in its program implementation, which increases its usage reliability. The used technology of evolutionary modeling is rather expanded in the mentioned algorithm. Firstly, the decimal chromosomes coding is used instead of the traditional binary coding. This has resulted from the fact that the chromosome genes condition is multiple and not binary. Moreover, this is due to the absence of the genetic operator of inversion in this algorithm. Secondly, a new genetic operator used for filtering has been implemented. This operator eliminates chromosomes that do not meet the required clusters quantity condition in a task. Such chromosomes can appear in the stochastic process of their evolution. The presented algorithm has been studied in a series of simulation experiments. As a result, it has been found that stabilization of splitting into clusters is reached when the number of completed generations of evolution is 200 and more, and the population size is rather small: from 150 chromosomes (in this case no considerable amount of random-access store is required). The calculations carried out on real data showed for this algorithm the high quality of clustering and the acceptable computing speed of the same order with the computing speed of SOM and \u201ck-means\u201d algorithms.<\/jats:p>","DOI":"10.32362\/2500-316x-2019-7-6-134-150","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T05:56:19Z","timestamp":1578894979000},"page":"134-150","source":"Crossref","is-referenced-by-count":14,"title":["Genetic clustering algorithm"],"prefix":"10.32362","volume":"7","author":[{"given":"M. A.","family":"Anfyorov","sequence":"first","affiliation":[{"name":"MIREA \u2013 Russian Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17429","published-online":{"date-parts":[[2020,1,10]]},"reference":[{"key":"ref1","unstructured":"Fuzzy systems, soft calculations and intellectual technologies: Proceedings of the VII All-Russian Scientific and Practical Conference. St. Petersburg, July 03-07, 2017. V. 2. St. Petersburg: Politekhnika-servis Publ., 2017. 210 p. (in Russ.)."},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Yudin V.N., Karpov L.E. Incompletely described objects in decision support. Programming and Computer Software. 2017;43(5):294-299.","DOI":"10.1134\/S0361768817050073"},{"key":"ref3","unstructured":"\u0410nfyorov M.\u0410. System optimization of high technologies. Izv. VUZ. \u0410viatsionnaya tekhnika = Russian Aeronautics. 2002;2:57-60 (in Russ.)."},{"key":"ref4","unstructured":"Batyrshin I.Z., Nedosekin \u0410.\u0410., Stetsko \u0410.\u0410., Tarasov V.B., Yazenin A.V., Yarushkina N.G. Fuzzy hybrid systems: theory and practice. Ed. N.G. Yarushkina. Moscow: Fizmatlit Publ., 2007. 207 p. (in Russ.)."},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"\u0410dzhemov S.S., Klenov N.V., Tereshonok M.V., Chirov D.S. The use of artificial neural networks for classification of signal sources in cognitive radio systems. Programming and Computer Software. 2016;42(3):121-128. 10.1134\/S0361768816030026","DOI":"10.1134\/S0361768816030026"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Hu Z., Bodyanskiy Y., Tyshchenko O.K. Self-learning procedures for a kernel fuzzy clustering system. Advances in Computing Science for Engineering and Education. 2019;754:487-497. http:\/\/dx.doi.org\/10.1007\/978-3-319-91008-6_49","DOI":"10.1007\/978-3-319-91008-6_49"},{"key":"ref7","unstructured":"\u0410nfyorov M.\u0410., Khannanov M.G. Cluster approach to design in CAM. In: Provedeniye nauchnykh issledovaniy v oblasti obrabotki, khraneniya, peredachi i zashchity informatsii = Conducting scientific research in the field of processing, storage, transmission and protection of information. Collection of scientific papers in 4 v. V. 3. Ul\u2019yanovsk: UlGTU Publ., 2009; pp. 60-65 (in Russ.)."},{"key":"ref8","unstructured":"Borozdina N.\u0410. The use of hierarchical cluster analysis for segmentation of consumers of the market of cellular communication. Molodoi uchenyi = Young scientist. 2016;29:365-367. (in Russ.). URL: https:\/\/moluch.ru\/archive\/133\/37358\/ (accessed November 13, 2019)."},{"key":"ref9","unstructured":"Dudarin P.V., Yarushkina N.G. Approaches to fuzzy and hierarchical clustering and classification of key process indicators of the strategic planning system of the Russian Federation. In: Proceedings of the VII All-Russian Scientific and Practical Conference \u201cFuzzy Systems, Soft Computing and Intelligent Technology\u201d. St. Petersburg, July 03\u201307, 2017. V. 2. SPb.: Polytekhnik servis Publ., 2017; pp. 65-73 (in Russ.)."},{"key":"ref10","unstructured":"Petukhova M.V. Clustering of borrowers at the level of defaults: Rating approach (regions of Siberian Federal District). Zhurnal Novoi ekonomicheskoi assotsiatsii = J. New Economic Association. 2012;4(16):71-102 (in Russ.)."},{"key":"ref11","unstructured":"\u0410nfyorov M.\u0410. Kohonen networks in a problem of identification of economically unstable regional structures. Proceedings of the XV All-Russian Scientific and Practical Conference \u201cNejroinformatika 13\u201d [Neuroinformatics-2013]. Moscow, 21\u201325 January, 2013. V. 3. \u041c.: NIYaU MIFI, 2013; pp. 177-184 (in Russ.)."},{"key":"ref12","unstructured":"\u0410nfyorov M.\u0410., Rashitova O.B. SADT modeling of the Russian Federation tax system. Ekonomika i upravlenie: nauchno-prakticheskii zhurnal = Economics and Management: Research and Practice Journal. 2015;2(124):94-101 (in Russ.)."},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez del Pozo R., Garc\u00eda-Lapresta J.L., P\u00e9rez-Rom\u00e1n D. Clustering U.S. 2016 presidential candidates through linguistic appraisals. Advances in Intelligent Systems and Computing. 2018;642:143-153. https:\/\/doi.org\/10.1007\/978-3-319-66824-6_13","DOI":"10.1007\/978-3-319-66824-6_13"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Kvostikov \u0410.V., Krylov \u0410.S., Kamalov U.R. Ultrasound image texture analysis for liver fibrosis stage diagnostics. Programming and Computer Software. 2015;41(5):273-278. https:\/\/doi.org\/10.1134\/S0361768815050059","DOI":"10.1134\/S0361768815050059"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Kumar S., Mishra S., Asthana P. Automated detection of acute leukemia using k-mean clustering algorithm. Advances in Intelligent Systems and Computing. 2018;554:655-670. https:\/\/doi.org\/10.1007\/978-981-10-3773-3_64","DOI":"10.1007\/978-981-10-3773-3_64"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Abadi S., Sari T.I., Maseleno A., Muslihudin M., Mat The K.S., Nasir B.M., Huda M., Ivanova N.L., Satria F. Application model of k-means clustering: insights into promotion strategy of vocational high school. International Journal of Engineering and Technology. 2018;7(2.27):182-187. http:\/\/dx.doi.org\/10.14419\/ijet.v7i2.11491","DOI":"10.14419\/ijet.v7i2.11491"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Hussain S., Atallah R., Kamsin A., Hazarika J. Classification, Clustering and Association Rule Mining in Educational Datasets Using Data Mining Tools: A Case Study. Advances in Intelligent Systems and Computing. 2019;765:196-211. https:\/\/doi.org\/10.1007\/978-3-319-91192-2_21","DOI":"10.1007\/978-3-319-91192-2_21"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Kharinov M.V. Pixel clustering for color image segmentation. Programming and Computer Software. 2015:41(5):258-266. https:\/\/doi.org\/10.1134\/S0361768815050047","DOI":"10.1134\/S0361768815050047"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"\u0410strakhantsev N.\u0410., Fedorenko D.G. Turdakov D.YU. Methods for automatic term recognition in domainspecific text collections: A survey. Programming and Computer Software. 2015;41(6):336-349. https:\/\/doi.org\/10.1134\/S036176881506002X","DOI":"10.1134\/S036176881506002X"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Lakhno V., Zaitsev S., Tkach Y., Petrenko T. Adaptive expert systems development for cyber attacks recognition in information educational systems on the basis of signs\u2019 clustering. Advances in Intelligent Systems and Computing. 2019;754:673-682. https:\/\/doi.org\/10.1007\/978-3-319-91008-6_66","DOI":"10.1007\/978-3-319-91008-6_66"},{"key":"ref21","unstructured":"Hartigan, J.A., Wong, M. A. Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society, Series C (Applied Statistics). 1979;28(1):100-108."},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Kohonen T. Self-Organizing Maps: 3rd edition. Berlin - New York: Springer-Verlag, 2001. 521 p.","DOI":"10.1007\/978-3-642-56927-2"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Zhang T., Ramakrishnan R., Livny M. BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD international conference on Management of data (SIGMOD \u201996). 1996; pp. 103-114. https:\/\/doi.org\/10.1145\/235968.233324","DOI":"10.1145\/235968.233324"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"P\u00e4ivinen N. Clustering with a minimum spanning tree of scale-free-like structure. Pattern Recognition Letters. 2005;26(7):921-930. https:\/\/doi.org\/10.1016\/j.patrec.2004.09.039","DOI":"10.1016\/j.patrec.2004.09.039"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Sudipto Guha, Rajeev Rastogi, Kyuseok Shim. CURE: An Efficient Clustering Algorithm for Large Databases. Information Systems. 1998;26(1):35-58. https:\/\/doi.org\/10.1016\/S0306-4379(01)00008-4","DOI":"10.1016\/S0306-4379(01)00008-4"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Sudipto Guha, Rajeev Rastogi, Kyuseok Shim. ROCK: a robust clustering algorithm for categorical attributes. Information Systems. 2000;25(5):345-366. https:\/\/doi.org\/10.1016\/S0306-4379(00)00022-3","DOI":"10.1016\/S0306-4379(00)00022-3"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Bodyanskiy Y., Didyk O. On-line robust fuzzy clustering for anomalies detection. Advances in Intelligent Systems and Computing. 2019;754:402-409. https:\/\/doi.org\/10.1007\/978-3-319-91008-6_40","DOI":"10.1007\/978-3-319-91008-6_40"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Ivanova E.V., Sokolinsky L.B. Parallel processing of very large databases with using distributed columnar indexes. Programming and Computer Software. 2017;43(3):131-144. https:\/\/doi.org\/10.1134\/S0361768817030069","DOI":"10.1134\/S0361768817030069"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Shao J., Yang Q., Schmidt B., Dang H-V., Kramer S. Scalable Clustering by Iterative Partitioning and Point Attractor Representation. ACM Transactions on Knowledge Discovery from Data. 2016;11(1):5:1-5:23. https:\/\/doi.org\/10.1145\/2934688","DOI":"10.1145\/2934688"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Songlei J.https:\/\/orcid.org\/0000-0001-5760-6431, Guansong P., Longbing C., Kai L., Hang G. CURE: Flexible Categorical Data Representation by Hierarchical Coupling Learning. IEEE Transactions on Knowledge and Data Engineering. 2019;31(5):853-866. https:\/\/doi.org\/10.1109\/TKDE.2018.2848902","DOI":"10.1109\/TKDE.2018.2848902"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Sheikholeslami G., Chatterjee S., Zhang A. WaveCluster: A Wavelet-Based Clustering Approach for Spatial Data. VLDB Journal. 2000;8(2-4):289-304. http:\/\/dx.doi.org\/10.1007\/s007780050009","DOI":"10.1007\/s007780050009"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Gionis A., Mannila H., Tsaparas P. Clustering Aggregation. ACM Transactions on Knowledge Discovery from Data. 2007;1(1):Article 4. 30 p. https:\/\/doi.org\/10.1145\/1217299.1217303","DOI":"10.1145\/1217299.1217303"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Wang C., She Z., Stantic B., Chi C.H., Cao L. Coupled Clustering Ensemble by Exploring Data Interdependence. ACM Transactions on Knowledge Discovery from Data. 2018;12(6):63:1-63:38. https:\/\/doi.org\/10.1145\/3230967","DOI":"10.1145\/3230967"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Zhang X., Zhang X., Liu H. Smart Multitask Bregman Clustering and Multitask Kernel Clustering. ACM Transactions on Knowledge Discovery from Data. 2015;10(1):8:1-8:29. https:\/\/doi.org\/10.1145\/2747879","DOI":"10.1145\/2747879"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Abasi A., Sajedi H. Fuzzy-clustering based data gathering in wireless sensor network. International Journal on Soft Computing (IJSC). 2016:7(1):1-15. https:\/\/doi.org\/10.5121\/ijsc.2016.7101","DOI":"10.5121\/ijsc.2016.7101"},{"key":"ref36","unstructured":"Gorbatkov S.\u0410., Rashitova O.B. Modeling of tax administrative decisions on the basis of Kohonen\u2019s neural networks. Informatsionnye tekhnologii = Information Technology. 2013;5:60-65 (in Russ.)."}],"container-title":["Russian Technological Journal"],"original-title":[],"link":[{"URL":"https:\/\/www.rtj-mirea.ru\/jour\/article\/viewFile\/187\/183","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T05:57:21Z","timestamp":1578895041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.rtj-mirea.ru\/jour\/article\/view\/187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,10]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,1,10]]}},"URL":"https:\/\/doi.org\/10.32362\/2500-316x-2019-7-6-134-150","relation":{},"ISSN":["2500-316X"],"issn-type":[{"value":"2500-316X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,10]]}}}