Article

Article title DEVELOPMENT OF SEMANTIC FILTERING METHODS BASED ON SOLVING THE TASK OF CLUSTERING BY BIOINSPIRED ALGORITHMS
Authors V. V. Markov, Yu. A. Kravchenko, M. A. Kuzmina
Section SECTION IV. DATA ANALYSIS AND KNOWLEDGE MANAGEMENT
Month, Year 04, 2018 @en
Index UDC 004.62
DOI
Abstract This article is devoted to solving the problems of theoretical knowledge management foundation development for the creation of multi-disciplinary intellectual information systems, semantic search, promoting the development of science promising areas and information technologies related to solving the problems of knowledge integration from heterogeneous distributed sources, the identification of the subject area and conceptual modeling, analysis and retrieval data. The urgency of solving this problem is to expand the application areas of application developed intelligent information systems for a range of class NP- complex urgent tasks. The proposed article considers the clustering problem. In order to improve the efficiency and quality of knowledge management solutions offered are the modified tasks clustering algorithms based on using bioinspired approaches. The developed modifications of the algorithms improve the quality of the solutions obtained. These studies in the field of semantic search constitute an important scientific direction within the framework of the problem of the knowledge management technologies development. The solution of the problem of clustering by means of using of bioinspired algorithms is considered herein. The following two new clustering algorithms: a combined algorithm based on the Artificial Bee Colony algorithm and the k-means algorithm, as well as the Ant clustering algorithm, are proposed. To study the algorithms obtained, their effectiveness and the results quality, software modules that simulate the proposed algorithms have been developed. The analysis of the obtained results, comparison with the classical algorithm, as well as conclusions about the effectiveness of new algorithms. are presented.

Download PDF

Keywords Semantic filtering; clustering; bioinspired algorithms; knowledge management; modification; combining.
References 1. Chubukova I.A. Data Mining: ucheb. posobie [Data Mining: tutorial]. Moscow: Internet-Universitet Informacionnykh Tekhnologiy; BINOM. Laboratoriya znaniy, 2006, 327 p.
2. Gladkov L.A., Kuretchik V.M., Kuretchik V.V., Sorokoletov P.V. Bioinspirirovannye metody v optimizacii: monografiya [Bioinspired methods in optimization: monograph]. Moscow: Fizmalit, 2009, 384 p.
3. Anop M.F., Katueva Ya.V., Mikhalichuk V.I. Algoritmy roya pchel i chastic v zadache obespecheniya nadezhnosti po postepennym otkazam [Algorithms of swarm of bees and particles in the problem of reliability on gradual failures], Nauka i obrazovanie [Science and education], 2015, No. 1, pp. 144-157.
4. Sorokoletov P.V. Metody i algoritmy prinyatiya resheniy na osnove bionicheskogo poiska: monografiya [Methods and algorithms of decision-making based on bionic search: monograph]. Taganrog: Izd-vo TTI YuFU, 2009, 352 p.
5. Kravchenko Y.A., Kureichik V.V. Knowledge management based on multi-agent simulation in informational systems, Conference proceedings. 8th IEEE International Conference “Application of Information and Communication Technologies – AICT 2014”. 15-17 October 2014, Astana, Kazakhstan, pp. 264-267.
6. Gladkov L.A., Gladkova N.V., Legebokov A.A. Organization of knowledge management based on hybrid intelligent methods, Advances in Intelligent Systems and Computing, 2015,
Vol. 349, pp. 107-112.
7. Kureychik V.M., Kureychik V.V., Rodzin S.I. Modeli parallelizma evolyucionnykh vychisleniy. [Models of parallelism of evolutionary calculations], Vestnik Rostovskogo gosudarstvennogo universiteta putey soobshcheniya [Vestnik RGUPS)], 2011, No. 3 (43), pp. 93-97.
8. Kureychik V.M., Kureychik V.V. Evolyucionnye, sinergeticheskie i gomeostaticheskie strategii v iskusstvennom intellekte: sostoyanie i perspektivy [Evolutionary, synergetic and homeostatic strategies in artificial intelligence: state and prospects], Novosti iskusstvennogo intellekta [News of artificial intelligence], 2000, No. 3, pp. 39-67.
9. Bova V.V., Kureichik V.V., Lezhebokov A.A. The integrated model of representation of problem-oriented knowledge in information systems, 8th IEEE International Conference on Application of Information and Communication Technologies, AICT 2014 – Conference Proceedings 8, 2014. С. 7035923, pp. 111-114.
10. Bova V.V., Kureychik V.V. Integrirovannaya podsistema gibridnogo i kombinirovannogo poiska v zadachakh proektirovaniya i upravleniya [Integrated subsystem of the hybrid and combined search in problems of design and management], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 12 (113), pp. 37-43.
11. Zaycev A.A., Kureychik V.V., Polupanov A.A. Obzor evolyucionnykh metodov optimizacii na osnove roevogo intellekta [Overview of evolutionary optimization techniques based on swarm intelligence], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 12 (113), pp. 7-12.
12. Kureychik V.V., Kureychik Vl.Vl. Bioispirirovannyy poisk pri proektirovanii i upravlenii [Biospherology search in the design and management], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 11 (136), pp. 178-183.
13. Gladkov L.A., Kureychik V.M., Kureychik V.V. Geneticheskie algoritmy [Genetic algorithm]. Moscow: Fizmatlit, 2006, 320 p.
14. Rodzin S.I., Kureychik V.V. Sostoyanie, problemy i perspektivy razvitiya bioevristik [State, problems and prospects of bio-heuristics development], Programmnye sistemy i vychislitel'nye metody [Software systems and computational methods], 2016, No. 2, pp. 158-172.
15. Rodzin S.I., Kureychik V.V. Teoreticheskie voprosy i sovremennye problemy razvitiya kognitivnykh bioinspirirovannyh algoritmov optimizacii [Theoretical questions and contemporary problems of the development of cognitive bio-inspired algorithms for optimization], Kibernetika i programmirovanie [Cybernetics and programming], 2017, No. 3, pp. 51-79.
16. Kureychik V.V., Rodzin S.I. O pravilakh predstavleniya resheniy v evolyucionnykh algoritmakh [About the rules for the submission of solutions in evolutionary algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2010, No. 7 (108), pp. 13-21.
17. Zaporozhec D.Yu., Kudaev A.Yu., Lezhebokov A.A. Mnogourovnevyy algoritm resheniya zadachi parametricheskoy optimizacii na osnove bioinspirirovannykh evristik [Multi-level algorithm for solving the problem of parametric optimization based on bioinspired heuristics], Izvestiya Kabardino-Balkarskogo nauchnogo centra RAN [Izvestiya of Kabardino-Balkar scientific center of RAS], 2013, No. 4 (54), pp. 21-28.
18. Kureychik V.M. Osobennosti postroeniya sistem podderzhki prinyatiya resheniy [Features of construction of systems of support of acceptance of decisions], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2012, No. 7 (132), pp. 92-98.
19. Kureychik V.M., Kazharov A.A. Ispol'zovanie shablonnykh resheniy v murav'inykh algoritmakh [The use of standard solutions in ant colony optimization algorithms], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2013, No. 7 (144), pp. 11-17.
20. Gladkov, L.A., Kravchenko Y.A., Kureichik V.V. Evolutionary Algorithm for Extremal Subsets Comprehension in Graphs, World Applied Sciences Journal, 2013, Vol. 27 (9), pp. 1212-1217.

Comments are closed.