CURRENT TRENDS AND BASIC METHODS OF RAW DATA INTELLIGENT ANALYSIS

Authors

  • O. Haidenko

Keywords:

intelligent information processing, data mining, electricity, railways

Abstract

Fundamental methods of the data mining, such as visualization and statistical methods, artificial intelligence, decision trees and genetic algorithm are considered. Fields of
their possible application in the task of railroad power supply system intellectualization are research.

References

1. Sang Jun Lee. A review of data mining techniques / Sang Jun Lee, Keng Siau // Industrial Management & Data Systems, Vol. 101 Iss: 1, pp.41–46
2. О. Haidenkо. Intelektualna obrobka baz znan hospodarstva elektropostachannya zaliznyc [Intelligentprocessing knowledge bases of railway power facilities] // Zbirnyk naukovyh prac’ DETUT: Serija «Transportni systemy i tehnologii’», vol. 28. – К.: 2016. – pp. 147-153.
3. Jim Gray. The Transaction Concept: Virtues and Limitations. Proceedings of the 7th International Conference on Very Large Databases, 1981, pp. 144–154.
4. Lu H. Effective Data Mining Using Neural Networks / Lu H., Setiono R., Liu H. // IEEE Transactions on Knowledge and Data Engineering – 1996, Vol. 8 No. 6. – pp. 957-961.
5. V. Hrechkin. Intelektualnye algoritmy obrabotki informacii v mnogokriterialnyh sistemah podderjki prinyatiya resheniy [Intelligent information processing algorithms in multiobjective decision support systems] // Vestnik Stavropolskogo gosudarstvennogo universiteta – 2010. – №70. – pp. 35-39.
6. Whittaker J. Graphical Models in Applied Multivariate Statistics // New York: Wiley – 1990.
7. Pearl J. Probabilistic Reasoning in Intelligent Systems // San Francisco, Calif.: Morgan Kaufmann – 1988.
8. Fayyad U.M. From Digitized Images to On-Line Catalogs: Data Mining a Sky Survey / Fayyad U.M., Djorgovski S.G., Weir N. // AI Magazine – 1996. – №17(2). – pp. 51–66.
9. Joshua Gould. Classification and Regression Trees (CART) Documentation. ftp://ftp.broad.mit.edu/pub/genepattern/modules/CART/broad.mit.edu:cancer.software.genepattern.module.analys is/00056/1/CART.pdf
10. Breiman L. Classification and regression trees. Monterey / Breiman L., Friedman J.H., Olshen R.A., Stone C.J. // CA: Wadsworth & Brooks/Cole Advanced Books &Software, 1984.
11. Breiman L. Classification and Regression Trees / Breiman L., Friedman J.H., Olshen R.A., Stone C.J. // Belmont, Calif.: Wadsworth, 1984.
12. Quinlan J. C4.5: Programs for Machine Learning / San Francisco, Calif.: Morgan Kaufmann, 1992.

Література:

1. Sang Jun Lee A review of data mining techniques / Sang Jun Lee, Keng Siau // Industrial Management & Data Systems, Vol. 101 Iss: 1, pp.41–46
2. О.С. Гайденк. Інтелектуальна обробка баз знань господарства електропостачання залізниць // Збірник наукових праць ДЕТУТ. Серія «Транспортні системи і технології». – Вип. 28. – К.: ДЕТУТ, 2016. – С. 147-153.
3. Jim Gray. The Transaction Concept: Virtues and Limitations. Proceedings of the 7th International Conference on Very Large Databases, 1981, pp. 144–154.
4. Lu H. Effective Data Mining Using Neural Networks / Lu H., Setiono R., Liu H. // IEEE Transactions on Knowledge and Data Engineering – 1996, Vol. 8 No. 6. – pp. 957-961.
5. В.А. Гречкин. Интеллектуальные алгоритмы обработки информации в многокритериальных системах поддержки принятия решений // Вестник Ставропольского государственного университета – 2010. – №70. – С. 35-39.
6. Whittaker J. Graphical Models in Applied Multivariate Statistics // New York: Wiley – 1990.
7. Pearl J. Probabilistic Reasoning in Intelligent Systems // San Francisco, Calif.: Morgan Kaufmann – 1988.
8. Fayyad U.M. From Digitized Images to On-Line Catalogs: Data Mining a Sky Survey / Fayyad U.M., Djorgovski S.G., Weir N. // AI Magazine – 1996. – №17(2). – pp. 51–66.
9. Joshua Gould Classification and Regression Trees (CART) Documentation [Електрон. ресурс]. – Режим доступу: ftp://ftp.broad.mit.edu/pub/genepattern/modules/CART/broad.mit.edu:cancer.software.genepattern.modu le.analysis/00056/1/CART.pdf.
10. Breiman L. Classification and regression trees. Monterey / Breiman L., Friedman J.H., Olshen R.A., Stone C.J. // CA: Wadsworth & Brooks/Cole Advanced Books &Software, 1984.
11. Breiman L. Classification and Regression Trees / Breiman L., Friedman J.H., Olshen R.A., Stone C.J. // Belmont, Calif.: Wadsworth, 1984.
12. Quinlan J. C4.5: Programs for Machine Learning / San Francisco, Calif.: Morgan Kaufmann, 1992.

Published

2018-11-02

How to Cite

Haidenko, O. (2018). CURRENT TRENDS AND BASIC METHODS OF RAW DATA INTELLIGENT ANALYSIS. Transport Systems and Technologies, (29), 184–188. Retrieved from https://tst.duit.in.ua/index.php/tst/article/view/81

Issue

Section

Information, telecommunication and resource saving technologies