Analogue complexing algorithm's usage in data mining

• Autorzy: Zubov D. , Ulshin V. , Gorbunov A.

Warianty tytułu

RU

Ispol'zovanie algoritma kompleksirovanija analogov v data mining

• Języki publikacji: EN

Abstrakty

EN

RU

Słowa kluczowe

EN

analogue complexing; algorithm; prognosis; Fibonacci number; geometrical series; meteorology; agrometeorology; data mining; linear interpretation

• Wydawca: -
• Czasopismo: Teka Komisji Motoryzacji i Energetyki Rolnictwa
• Rocznik: 2010
• Tom: 10D
• Opis fizyczny: p.81-90,fig.,ref.

Twórcy

autor

Zubov D.

• Volodymyr Dahl East-Ukrainian University, Lugansk, Ukraine

autor

Ulshin V.

autor

Gorbunov A.

Bibliografia

• 1. German O.V. 1995.: Introduction into expert systems theory and data mining. DesignPRO Press Inc., Minsk.
• 2. Ivakhnenko G. 2008.: Short-Term Process Forecasting by Analogues Complexing GMDH Algorithm. In: Proceedings of 2nd Int. Conf. on Inductive Modeling, Kyiv, Ukraine, Sept. 15-19, p.241-245.
• 3. Ivakhnenko A.G. 1981.: Inductive Method of Self-organizing Models of the Intricate Systems. Kiev, Ukraine.
• 4. Zubov D., Vlasov Y., Grigorenko M. 2008.: Method of the Decade Air’s Temperature Long- Range Prognosis with Robust Inductive Models and Analogue Principle. In: Proceedings of 2nd Int. Conf. on Inductive Modeling, Kyiv, Ukraine, Sept. 15-19, p. 263-266.
• 5. Godfrey C. Onwubolul, Petr Buryan, Sitaram Garimella, Visagaperuman Ramachandran, Viti Buadromo and Ajith Abraham. 2007.: Self-Organizing Data Mining for Weather Forecasting. Proceedings of the First European Conference on Data Mining. Lisboa, IADIS Press, p. 81-88.
• 6. Cofino A.S., Gutierrez J.M. 2003.: Implementation of Data Mining Techniques for Meteorological Applications. Realizing Teracomputing. W.Zwieflhofer and N.Kreitz, editors. World Scientific, p. 215-240.
• 7. Gavrilova T.A., Chervinskaya K.R. 1992.: Knowledge acquisition and structuring for expert systems. Moscow, Radio and communication Press, 200 p.
• 8. Kirdin A.N., Novokhodko A.Y., Tsaregorodcev V.G. 1998.: Hidden parameters and transposed regression. Chapter 7 in Neuron-informatics book. Novosibirsk, Science Press, 296 p.
• 9. Gavrilova T.A., Khoroshevsky V.F. 2000.: Knowledge bases of intellectual systems. St. Petersburg, Piter Press, 384 p.
• 10. Yushkov A.V., Zhurenkov O.V. 2005.: Using of the Analogue Complexing of Parameters of Space-Temporal Distribution of the EAS Cherenkov Light for the Analysis of the Mass Composition of Cosmic Rays. 29th International Cosmic Ray Conference, Pune, 6. -p. 81-84.
• 11. Ivakhnenko A.G. 1991.: An Inductive Sorting Method for the Forecast of Multidimensional Random Processes and Analog Events with the Method of Analog Forecast Complexing . Pattern Recognition and Image Analysis, Vol. 1, N 1, p. 101-107.
• 12. Ivakhnenko A.G., Kovalish V.V., Tetko I.V., Luik A.I., Ivakhnenko G.A., Ivakhnenko N.A. 1999.: Self-Organization of Neural Networks with Active Neurons for Bioactivity of Chemical Compounds Forecasting by Analogues Complexing GMDH algorithm. Report at the ICANN’99 Conference, London.
• 13. Johann-Adolf Mueller and Frank Lemke 1999.: Self-Organising Data Mining. An Intelligent Approach To Extract Knowledge From Data. 1st Edition. Berlin, Dresden, Trafford Publishing Press, 260 p.
• 14. Madala H.R., Ivakhnenko A.G. 1994.: Inductive Learning Algorithms for Complex System Modeling. CRC Press, 368 p.
• 15. Statistical Data Mining and Knowledge Discovery (2004) Edited by Bozdogan H. CRC Press, 595 p.