Abstract: The development of technology, computers and
the Internet has significantly contributed to easier
organization of data, which would also become more
useful if turned into information and knowledge.
Knowledge can refer to the users of products and/or
services, or the market in which the company operates.
From the perspective of the enterprise, the business
intelligence system places the primary emphasis on the
users of products and services. Data mining combines
concepts, tools and algorithms of machine learning and
statistics to analyze very large data sets, so as to gain
insight, understanding and effective knowledge, and it is
applied for this purpose in many organizations. In the
recent years, the market of data mining tools has become
more and more flooded, with more than fifty commercial
tools. The tools like SPSS PASW Modeler (Clementine),
Excel, Rapid Miner, SAS, SAS Enterprise Miner and many
others have been much more used until recently.
However, the R language is increasingly taking over this
role today. R is statistical software, and an objectoriented
high-level programming language used for data
analysis, which includes a large number of statistical
procedures such as t-test, chi-square test, standard linear
models, instrumental variables estimation, local
regression polynomials, etc. The R language has built-in
functions for the nearest neighbor method allowing the
automatic classification, the asociation rules showing the
connection probability between two or more events,
decision tree models, numerous methods of single and
multiple regression, and many others, which makes it a
very high-quality tool in data mining techniques.