Article

Article title CIRCUIT PARTITIONING PROBLEM CLUSTERING METHOD
Authors V. M. Kureichik, I. B. Safronenkova
Section SECTION I. DESIGN AUTOMATION
Month, Year 04, 2018 @en
Index UDC 004.896
DOI
Abstract The development of new intelligent information processing methods is caused by the onrush of computer equipment. The present article shows that an automated domain ontology development is important today. Domain ontology is often used as a knowledge base “frame” in Intelligent Decision Support Systems (IDSS). The problem of engineering software benchmarking study for the purpose of fitting for task type and computational resources is important today. This problem is often solved by IDSS. It"s worth to remark that a manual ontology development is a time consuming and expensive process. Because of a great variety of circuit partitioning problem formulization, clustering is a necessary step of automated circuit partitioning problem ontology development. The automated clustering partitioning problem is caused by the problem of complex data comparison. This data is usually presented by different dimension structure. The goal of this work is the development of circuit partitioning problem clustering method based on adjacency matrix unification. The hypergraph model of circuit representation has been chosen, the circuit partitioning problem formalized. The novelty of proposed method is a different dimension matrix unification procedure inclusion into the typical clustering algorithm. The partitioning problem clustering experiments have been held. At the present stage of investigation we can conclude that the proposed method has a low computational complexity. The fundamental difference of developed method is the clustering of circuit partitioning problems that include different dimension matrix in their formalization. This permits to lead clustering automatically. The proposed method is focused on efficiency improvement of automated domain ontology development.

Download PDF

Keywords Clustering, ontology; circuit partitioning; matrix; attribute vector; hypergraph circuit model; matrix similarity
References 1. Fond sodeystviya innovatsiyam [The Fund for the promotion of innovation]. Available at:http://fasie.ru (accessed 28 May 2018).
2. Kureichik V, Safronenkova I. Integrated Algorithm of the Domain Ontology Development. In: Silhavy, R., Senkerik, R., Kominkova, O.Z., Prokopova, Z., Silhavy, P. (eds.), Artificial Intelligence Trends in Intelligent Systems. AISC, Springer, Cham, 2017, Vol. 573, pp. 146-155.
3. Noy N., McGuinness D.: Ontology development 101: a guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical report KSL-01-05 and Stanford Medical Informatics Technical report SMI-2001- 0880 (2001).
4. Platov A.V., Poleshchuk E.A. Metody avtomaticheskogo postroeniya ontologiy [Methods of automatic ontology building], Programmnye produkty i sistemy [Software products and systems], 2016, No. 2 (114), pp. 47-52.
5. Gavrilova T.A., Kudryavtsev D.V., Muromtsev D.I. Inzheneriya znaniy. Modeli i metody [Knowledge engineering. Models and methods]. Saint Petersburg: Lan', 2016, 324 p.
6. Sidorkina I.G. Sistemy iskusstvennogo intellekta: ucheb. posobie [Artificial intelligence systems: tutorial]. Moscow: KNORUS, 2015, 248 p.
7. Ul'man Dzh., Radzharaman A., Leskovets Yu. Analiz bol'shikh naborov dannykh [Analysis of large data sets]. Moscow: DMK-Press, 2016, 498 p.
8. Baturkin S.A., Baturkina E.Yu., Zimenko V.A., Siginov I.V. Statisticheskie algoritmy klasterizatsii dannykh v adaptivnykh obuchayushchikh sistemakh [Statistical algorithms of data clustering in adaptive learning systems], Vestnik RGRTU [Bulletin of RSTU], 2010, No. 1 (31), pp. 82-85.
9. Sabhia Firdaus, Md. Ashraf Uddin. A Survey on Clustering Algorithms and Complexity Analysis, International Journal of Computer Science Issues, March 2015, Vol. 12, Issue 2, pp. 62-85.
10. Norenkov I.P. Osnovy avtomatizirovannogo proektirovaniya [Fundamentals of computer-aided design]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2006, 360 p.
11. Mylov G.V., Medvedev A.M., Semenov P.V., Konstantinov P.N. Nauchnye osnovy proektirovaniya mezhsoedineniy na pechatnykh platakh [Scientific bases of design of interconnections on printed circuit boards]. Moscow: Goryachaya liniya – Telekom, 2016, 98 p.
12. Kureychik V.M. Matematicheskoe obespechenie konstruktorskogo i tekhnologicheskogo proektirovaniya s primeneniem SAPR: ucheb. dlya vuzov [Mathematical support of design and technological design using CAD: textbook for universities]. Moscow: Radio i svyaz', 1990, 352 p.
13. Kureychik V.M., Safronenkova I.B. Razrabotka arkhitektury SPPR po vyboru metodov resheniya zadach komponovki [Developing the architecture of a DSS for the selection of methods for solving problems in the layout], Informatsionnye tekhnologii [Information technology], 2017, Vol. 23, No. 10, pp. 736-741.
14. Sorokoletov P.V. Postroenie intellektual'nykh sistem podderzhki prinyatiya resheniy [The construction of intellectual systems of support of decision-making], Izvestiya YuFU. Tekhnicheskie nauki [Izvestiya SFedU. Engineering Sciences], 2009, No. 4 (93), pp. 117-124.
15. Karypis G., Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs, SIAM J. Scien. Comput., 1999, Vol. 20 (1).
16. Segaran T. Programmiruem kollektivnyy razum [Programmable collective intelligence]: transl. from engl. Saint Petersburg: Simvol-Plyus, 2015, 368 p.
17. Madhulatha S.: An overview on clustering methods, IOSR Journal of Engineering, Apr. 2012, Vol. 2 (4), pp: 719-725.
18. Maimon O., Rokach L. Data Mining and Knowledge Discovery Handbook. Springer US, 2010.
19. Tikhonov A.N. TSvetkov V.Ya. Metody i sistemy podderzhki prinyatiya resheniy [Methods and systems of decision support]. Moscow: MAKS Press, 2001.
20. Greshilov A.A. Matematicheskie metody prinyatiya resheniy [Mathematical methods of decision-making]. Moscow: Izd-vo MGTU im. N.E. Baumana, 2006, 584 p.

Comments are closed.