Data mining is a set of techniques and methods relating to the extraction of knowledge from large amounts of data (through automatic or semi-automatic methods) and further scientific, industrial or operational use of that knowledge. Data mining is closely related to the statistics as an applied mathematical discipline with an analysis of data that could be defined as the extraction of useful information from data.The only difference between the two disciplines is that data mining is a new discipline that is related to significant or large data sets. R is an object-oriented programming language. This means that everything what is done with R can be saved as an object. Every object has a class. It describes what the object contains and what each function does. Application of R as a programming language and statistical software is much more than a supplement to Stata, SAS, and SPSS. Although it is more difficult to learn, the biggest advantage of R is its free-of-charge feature and the wealth of specialized application packages and libraries for a huge number of statistical, mathematical and other methods. R is a simple, but very powerful data mining and statistical data processing tool and once "discovered", it provides users with an entirely new, rich and powerful tool applicable in almost every field of research
| Published in | American Journal of Software Engineering and Applications (Volume 2, Issue 1) |
| DOI | 10.11648/j.ajsea.20130201.12 |
| Page(s) | 7-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2013. Published by Science Publishing Group |
R Language, Data Mining Techniques, Statistics
| [1] | M. J. Crawley, The R book, Imperial College London at Silwood Park, UK, John Wiley and Sons, Ltd 2007. |
| [2] | Dalgaard, P. Introductory Statistics with R, New York, Springer-Verlag, 2002. |
| [3] | Krause, A. and Olson, M. The Basics of S and S-PLUS, New York, Springer-Verlag, 2000. |
| [4] | McCulloch, C.E. and Searle, S.R. Generalized, Linear and Mixed Models, New York: John, 2001. |
APA Style
Sonja Pravilovic. (2013). R Language in Data Mining Techniques and Statistics. American Journal of Software Engineering and Applications, 2(1), 7-12. https://doi.org/10.11648/j.ajsea.20130201.12
ACS Style
Sonja Pravilovic. R Language in Data Mining Techniques and Statistics. Am. J. Softw. Eng. Appl. 2013, 2(1), 7-12. doi: 10.11648/j.ajsea.20130201.12
AMA Style
Sonja Pravilovic. R Language in Data Mining Techniques and Statistics. Am J Softw Eng Appl. 2013;2(1):7-12. doi: 10.11648/j.ajsea.20130201.12
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Download@article{10.11648/j.ajsea.20130201.12,
author = {Sonja Pravilovic},
title = {R Language in Data Mining Techniques and Statistics},
journal = {American Journal of Software Engineering and Applications},
volume = {2},
number = {1},
pages = {7-12},
doi = {10.11648/j.ajsea.20130201.12},
url = {https://doi.org/10.11648/j.ajsea.20130201.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajsea.20130201.12},
abstract = {Data mining is a set of techniques and methods relating to the extraction of knowledge from large amounts of data (through automatic or semi-automatic methods) and further scientific, industrial or operational use of that knowledge. Data mining is closely related to the statistics as an applied mathematical discipline with an analysis of data that could be defined as the extraction of useful information from data.The only difference between the two disciplines is that data mining is a new discipline that is related to significant or large data sets. R is an object-oriented programming language. This means that everything what is done with R can be saved as an object. Every object has a class. It describes what the object contains and what each function does. Application of R as a programming language and statistical software is much more than a supplement to Stata, SAS, and SPSS. Although it is more difficult to learn, the biggest advantage of R is its free-of-charge feature and the wealth of specialized application packages and libraries for a huge number of statistical, mathematical and other methods. R is a simple, but very powerful data mining and statistical data processing tool and once "discovered", it provides users with an entirely new, rich and powerful tool applicable in almost every field of research},
year = {2013}
}
TY - JOUR T1 - R Language in Data Mining Techniques and Statistics AU - Sonja Pravilovic Y1 - 2013/02/20 PY - 2013 N1 - https://doi.org/10.11648/j.ajsea.20130201.12 DO - 10.11648/j.ajsea.20130201.12 T2 - American Journal of Software Engineering and Applications JF - American Journal of Software Engineering and Applications JO - American Journal of Software Engineering and Applications SP - 7 EP - 12 PB - Science Publishing Group SN - 2327-249X UR - https://doi.org/10.11648/j.ajsea.20130201.12 AB - Data mining is a set of techniques and methods relating to the extraction of knowledge from large amounts of data (through automatic or semi-automatic methods) and further scientific, industrial or operational use of that knowledge. Data mining is closely related to the statistics as an applied mathematical discipline with an analysis of data that could be defined as the extraction of useful information from data.The only difference between the two disciplines is that data mining is a new discipline that is related to significant or large data sets. R is an object-oriented programming language. This means that everything what is done with R can be saved as an object. Every object has a class. It describes what the object contains and what each function does. Application of R as a programming language and statistical software is much more than a supplement to Stata, SAS, and SPSS. Although it is more difficult to learn, the biggest advantage of R is its free-of-charge feature and the wealth of specialized application packages and libraries for a huge number of statistical, mathematical and other methods. R is a simple, but very powerful data mining and statistical data processing tool and once "discovered", it provides users with an entirely new, rich and powerful tool applicable in almost every field of research VL - 2 IS - 1 ER -
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