EXTRACTING KNOWLEDGE FROM DATABASES THROUGH NEURO-FUZZY MODEL
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Abstract
Briefly discusses the technology of extraction of knowledge and types of extracted knowledge. A method is proposed for extracting knowledge represented as sets of association rules “IF ..., THEN ...”. The method is implemented using neural-fuzzy modeling of the subject area. We describe the techniques for constructing a fuzzy logical model of the analyzed domain and how to use neural networks to highlight fuzzy rules. Considered software for constructing a fuzzy model. An algorithm for extracting knowledge from the databases being analyzed is described.
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References
Piatetsky-Shapiro, G. Knowledge Discovery in Databases / G. Piatetsky-Shapiro, W.J. Frawley. – AAAI/MIT Press, 1991.
Wittern, I.H. Data Mining: practical machine learning tools and techniques / I.H. Wittern, E. Frank. – 2 ed. – Elsevier, 2016.
Aggarwal, C. Data classification: algorithms and applications / C. Aggarwal. – CRC Press, 2014.
Флах П. Машинное обучение. Наука и искусство построения алгоритмов, которые извлекают знания из данных / П. Флах. – М. : ДМК, 2017.
Kasko, B. Neural networks and Fuzzy Systems / B. Kasko. – NY. : Printice – Hall, 1992.
fuzzyTECH®INFORM GmbH. – Режим доступа http://www.fuzzytech.com/. – Дата доступа: 13.06.2018.
Von Altrock, C. Fuzzy Logic and NeuroFuzzy Application Explained / C. Von Altrock. – Prentice – Hall, 1995.