Excellent six open source data mining tools

Original Author:  Chandan Goopta. [Chandan Goopta is a data research expert from the University of Kathmandu (Nepal Capital) dedicated to building intelligent algorithms for affective analysis. ]

original link:http://thenewstack.io/six-of-the-best-open-source-data-mining-tools/

In this day and age, it is no exaggeration to say that data is money.

As the transition to an application-based domain, the data represents exponential growth. However, most of the data is unstructured, so it requires a program and method to extract useful information from it and convert it into an understandable, usable form. In the Data Mining task, there are a lot of tools to use, such as artificial intelligence, machine learning, and other technologies to extract data.
Here are six powerful open source data mining tools that you recommend:
1, RapidMiner

The tool is written in the Java language and provides advanced analysis techniques through a template-based framework. The best thing about this tool is that users don't have to write any code. It is provided as a service, not as a local software. It is worth mentioning that the tool is ranked top of the data Mining tool list.
In addition to data mining, RapidMiner also provides features such as data preprocessing and visualization, predictive analysis and statistical modeling, evaluation, and deployment. What's more, it also provides learning schemes, models, and algorithms from Weka (an intelligent analysis environment) and R scripts.
RapidMiner distribution under the AGPL Open source license, can download from SourceForge. SourceForge is a centralized site for developer management, with a large number of open source projects in place, including the mediawiki used by Wikipedia.
2, WEKA

The native non-Java version of Weka was developed primarily for the purpose of analyzing agricultural field data. The tool, based on the Java version, is very complex and is applied in many different applications, including data analysis and predictive modeling visualization and algorithms. The advantage compared to RapidMiner is that it is free under the GNU General Public License because users can choose to customize them according to their preferences.
Weka supports a variety of standard data mining tasks, including data preprocessing, collection, classification, regression analysis, visualization, and feature selection.
After adding a sequence model, Weka will become more powerful, but it is not currently included.
3, R-programming

What would you think if I told you the R project, a GNU project, was written by R (r-programming abbreviation, hereinafter collectively called R). It is mainly written in C and Fortran language, and many modules are written by R, this is a programming language and software environment for statistical calculation and mapping of free software. R language is widely used in data mining, and in the development of statistical software and data analysis. In recent years, ease of use and scalability have also greatly improved the visibility of R.
In addition to data, it provides statistical and cartographic techniques, including linear and non-linear modeling, classical statistical testing, time series analysis, classification, collection, and so on.
4, Orange

Python is popular because it is easy to learn and powerful. If you're a python developer, there's nothing more appropriate than orange when it comes to finding a tool to work with. It is a powerful open source tool based on the Python language and applies to both beginners and expert-level gods.
In addition, you will definitely fall in love with this tool for visual programming and Python scripting. It has not only machine learning components, but also the addition of biological information and text mining, can be said to be full of data analysis of various functions.
5, Knime

Data processing is mainly three parts: extraction, conversion and loading. And these three knime can be done. Knime provides you with a graphical user interface for processing data nodes. It is an open source data analysis, reporting and synthesis platform, and integrates various machine learning components and data mining through its modular data flow concept, and has attracted the attention of business intelligence and financial data analysis.
Knime is based on Eclipse, written in Java, and easy to extend and complement plug-ins. Its additional functionality can be added at any time, and its large number of data integration modules are already included in the core version.
6, NLTK

When it comes to language processing tasks, nothing can defeat NLTK. NLTK provides a language processing tool, including data mining, machine learning, data capture, emotion analysis and other language processing tasks. All you have to do is install NLTK, and then drag a package to your favorite task and you can do something else. Because it's written in Python, you can build your application on it and customize its small tasks.

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