Original author:Chandan Goopta.Chandan Goopta is a data researcher at Kathmandu University who works on building intelligent algorithms for sentiment analysis.]

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

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

With the transition to an app-based world, data has grown exponentially. However, much of the data is unstructured, so it requires a program and method to extract useful information from it and transform it into an understandable, usable form. In data mining, there are a number of tools available to extract data using artificial intelligence, machine learning, and other techniques.

Here are six powerful open source data mining tools:

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 you don’t have to write any code. It is offered as a service, not as native software. It’s worth mentioning that the tool is number one on the data mining tool list.

In addition to data mining, RapidMiner provides features such as data preprocessing and visualization, predictive analysis and statistical modeling, evaluation and deployment. Even better, it provides learning schemes, models, and algorithms from WEKA (an intelligent analysis environment) and R scripts.

RapidMiner is distributed under the AGPL open Source license and can be downloaded from SourceForge. SourceForge is a centralized place for developers to manage their development, hosting a number of open source projects, including MediaWiki, which wikipedia uses.

2, WEKA



The native non-Java version of WEKA was developed primarily to analyze agricultural data. Based on the Java version, the tool is very complex and is used in many different applications, including visualization and algorithms for data analysis and predictive modeling. The advantage over RapidMiner is that it is free under the GNU General Public License because users can customize it to their liking.

WEKA supports a variety of standard data mining tasks, including data preprocessing, collection, classification, regression analysis, visualization, and feature selection.

WEKA will become more powerful with the addition of sequence modeling, but is not currently included.

3, R – Programming



What if I told you that project R, a GNU project, was written by R itself? It is written primarily in C and FORTRAN, and many modules are written in R, a free program for statistical calculations and charting for programming languages and software environments. R language is widely used in data mining, development of statistical software and data analysis. Ease of use and extensibility have also greatly increased R’s popularity in recent years.

In addition to data, it also provides statistical and cartographic techniques, including linear and nonlinear modeling, classical statistical testing, time series analysis, classification, collection, and more.

4, Orange



Python is popular because it is easy to learn and powerful. If you’re a Python developer, when it comes to finding a tool to work with, there’s no better place to start than Orange. It is a powerful open source tool based on Python and is suitable for beginners and experts alike.

Plus, you’ll love the tool’s visual programming and Python scripting. It has not only machine learning components, but also biological information and text mining, which is full of data analysis capabilities.

5, KNIME



There are three main parts of data processing: extraction, transformation and loading. KNIME can do all three. KNIME provides you with a graphical user interface for processing data nodes. It is an open source data analysis, reporting, and integration platform that integrates various machine learning components and data mining with its flow-of-water concept of modular data and has captured the attention of business intelligence and financial data analytics.

KNIME is eclipse-based, written in Java, and easy to extend and complement plug-ins. Additional features can be added at any time, and a large number of its data integration modules are included in the core version.

6, me



When it comes to language processing tasks, nothing beats NLTK. NLTK provides a language processing tool, including data mining, machine learning, data capture, sentiment analysis and other language processing tasks. All you need to do is install NLTK, drag and drop a package into your favorite task, and you can do something else. Because it’s written in Python, you can build applications on it and customize its small tasks.