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Ciência e Natura 2015, 37 6-2

Author: Mehri Haghighi

Source: http://www.redalyc.org/articulo.oa?id=467547683010


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Ciência e Natura ISSN: 0100-8307 cienciaenaturarevista@gmail.com Universidade Federal de Santa Maria Brasil Haghighi, Mehri Data Mining and Machine Learning: an Overview of Classifiers Ciência e Natura, vol.
37, núm.
6-2, 2015, pp.
76-86 Universidade Federal de Santa Maria Santa Maria, Brasil Available in: http:--www.redalyc.org-articulo.oa?id=467547683010 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative 76 Ciência e Natura, Santa Maria, v.
37 Part 2 2015, p.
76−86 ISSN impressa: 0100-8307 ISSN on-line: 2179-460X Data Mining and Machine Learning: an Overview of Classifiers Mehri Haghighi Department of Computer Engineering, Payam Noor University, Sosangerd, Iran Abstract At the same time of information age, digital revolution has made necessary using some of technologies to analyze most of essential information.
Data mining is a technique to make sense to the available data.
The aim of data mining is extracting the information from a vast volume of data and transforming them into a comprehensible form for human.
For this purpose, machine learning methods are used to classify data.
In this study, we discuss six popular and useful classifiers in the data mining process. Keywords: Machine Learning, Classification, Support Vector Machine, decision Tree,K-nearst Neibora, bagging, Boosting. Recebido: dia-mês-ano Aceito: dia-mês-ano 77 1 Introduction A t the same time of information age, digital revolution has made necessary using some of technologies to analyze most of essential information.
Data mining, is a technique to make sense to the available data. This technology has been became popular in the recent years more than before.
Data mining is a set of techniques that allow us to move beyond the ordinary data proces...





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