As data mining tasks become more crucial day by day, data mining tools and data mining techniques are rapidly increasing. Currently, there have been developed a significant number of software that provide scientists and analysts with the appropriate tools to perform data mining tasks and apply mining algorithms.
Some of the most frequently used technologies for data mining are programming languages such as R, Python, Java, Scala and Julia. Also, there are plenty of desktop software that are used for data mining activities. The list describes five of the most widely used tools for data mining.
RStudio is a free and open-source integrated development environment for R. R stands for a language and environment used in statistical computing and graphics. Based on the S language and environment, and is considered as a different implementation of S. Even though several differences between the two languages or environments can be observed, the same code written for S runs respectively under R.
A wide range of statistical packages and mining algorithms is offered by R as well as graphical techniques. Some of them include linear and nonlinear modelling, classical statistical tests, time-series analysis, classification and clustering. Being an open source, R is highly extensible and a useful tool for the statistical methodology research and data mining techniques.
R is engaged with a great ease of mathematical symbols and formulas while it also participates in the production of well-designed publication-quality plots. R allows the user to be fully in charge, although the defaults and the design choices offered in graphics are so well-designed that can be used without the user’s involvement. In order for R code to be executed, a user can compile and run R code on multiple UNIX platforms and other equivalent systems, including FreeBSD, Linux, Windows and MacOS.
Another widely used data mining tool is PyCharm, which is an integrated development environment for python. Python is a strong programming language that can be easily learned. Its high-level data structures are quite effective whereas its approach to object-oriented programming is simple but efficient. Python can be perceived as the perfect language for scripting and high-speed application in many areas on the majority of platforms thanks to its elegant syntax, dynamic typing and interpreted nature. All of the Python’s features and libraries can be found and distributed for free on the Python Web site, and other directories.
Apart from Python’s main components, Python distributes a great number of third party modules, programs and tools, as well as extra documentation. The Python interpreter can be easily expanded with the addition of new functions and data types implemented in C or C++.
Weka is an open source data mining software. Its most crucial function is to collect machine learning algorithms employed for data mining tasks. The algorithms can either be applied directly to a dataset or called from your Java code. The tools entailed in Weka can perform data pre-processing, classification, regression, clustering, association rules, and visualisation.
Weka is also suitable for creating new machine learning schemes and it is an open source software issued under the GNU General Public License.
RapidMiner Studio through its well-designed environment and convenient usage has the power and potential to quickly complete any predictive analytic procedure. This fully equipped tool supplies users with various predefined data preparation and machine learning algorithms that support all of the former’s data science projects.
As with the tools described in previous paragraphs, RapidMiner can perform data pre-processing, classification, regression, clustering, association rules and visualisation tasks.
KNIME is also an open source software that serves data analysis, reporting and integration processes through its modular data pipe-lining concept. All tasks in KNIME can be easily accomplished by the final user because of its easily operated graphical environment. Although written in Java and based on Eclipse, KNIME can evolve it functionality engaging extensions to add plugins.
KNIME effectively cooperates with many other open-source projects such as machine learning algorithms from Weka and the statistics package from R project.