25+ Free Data Mining Tools for Better Analysis

In today’s world, data is money. Most of the data is unstructured and therefore, you need an efficient method to extract mandatory information and transform it in usable or understandable format. There comes the role of data mining software. Along with raw analysis, these tools are also equipped with data management aspects, database, data-preprocessing model, complexity consideration, visualization and online updating.

There are plenty of tools available out there that performs data mining tasks using advanced techniques such as business learning, artificial intelligence and machine learning. Most of these tools are paid. We also understand that all business can’t afford these expensive premium tools, that why we have come with the mega list of free data mining tools that will help you dig deeper and understand your data in a much better way.

27. mply


mply is machine learning python built on top of GNU scientific library and NumPy/SciPy. It provides a wide range of machine learning methods for both supervised and unsupervised problems. It features classification, regression, clustering, dimensionality reduction and wavelet submodule.

26. Jubatus


Jubatus is a library and framework for distributed online machine learning. It can handle 100,000+ data per second using commodity hardware clusters. Jubatus supports classification, clustering, regression, graph analysis and it updates the model instantaneously just after receiving the data.

25. PyBrain


PyBrain is a powerful, flexible and modular machine learning library for Python. It contains algorithms for neural networks, unsupervised learning, reinforcement learning and evolution.

24. MiningMart


MiningMart approach is based on the preprocessing chains that are developed by experienced users. It has developed an operational meta language for describing data and operators. MiningMart has also prepared the first cases of KDD.

23. KEEL


KEEL is an open source Java software tool to access algorithms for data mining problems including clustering, classification, pattern mining regression and more. It is packed with classical knowledge extraction algorithms, feature selection, preprocessing techniques, computational intelligence and hybrid models like evolutionary neural networks, genetic fuzzy systems and more.

22. Fityk


Fityk is a data processing and curve fitting software primarily used for analyzing data from chromatography, photoelectron spectroscopy, powder diffraction and other experimental techniques. Furthermore, it can be used for any task that requires fitting a curve to 2d data.

21. CMSR Data Miner

CMSR Data Miner

CMSR data miner provides an integrated environment for predictive modeling, data visualization, rule based model evaluation, segmentation and statistical data analysis. The main feature includes neural clustering, database scoring, radial basis function, hotspot drill down, decision tree classification, Cross-sell Basket Analysis and more.

20. Pandas


Pandas is powerful and flexible Python library for data analysis and manipulation. With pandas, you can easily handle missing data, convert ragged and differently indexed data in other form, reshape, merge, join or pivot large data sets. It also supports frequency conversion, moving window linear regressions, lagging and data shifting.

19. Shogun


Shogun is a large scale machine learning toolbox that provides unified and effective machine learning methods. It allows you to combine algorithm classes, multiple data representation and general purpose tools. You can use the toolbox through a unified interface from C++, Java, R, Python, C#, Lua etc.

18. SCaVis


SCaVis is scientific computation and visualization environment for data analysis and data visualization. It can be used with large numerical data volumes and can run on any Java installed platform. The program is packed with many open source packages into a coherent interface using the concept of data scripting.



MALLET is a Java based package for document classification, information extraction, clustering, topic modeling, natural language processing, machine learning and more. It includes numerous algorithms for calculating performance using different commonly used metrics. Also, there is an add-on package for this tool called GRMM that contains support for graphical models.



CLUTO is a software package for clustering low and high dimensional datasets. It features multiple classes of clustering algorithms, distance functions, merging schemes, visualization capabilities and various methods for summarizing the clusters.

15. Databionic ESOM Tools

Databionic ESOM Tools

The databionic ESOM tools is a set of program for performing data mining task like clustering, classification and visualization. It features interactive, exploitative data analysis, animated visualization, creation of non-redundant U-maps, creation of ESOM classifier, automated application to new data and more.

14. Rattle


Rattle gives you a logical interface for data mining. It is based on free statistical language R using the Gnome graphical interface. The primary aim of this tool is to provide intuitive interface which takes you through the basic of data mining and illustrate the R code which is use to achieve this.

13. Apache Mahout

Apache Mahout

Apache Mahout is scalable machine learning and data mining platform. Here scalable refers to large data set and vibrant community. It supports mainly 3 use cases i.e. recommendation mining, clustering and classification.

12. Tanagra


Tanagra is a data mining tool for academic and research purpose. It includes several data mining techniques such as data analysis, machine learning, statistical learning and more.  The software act as an experimental platform where you can add your own mining method to compare the performance.

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11. PSPP


PSPP is a program (GNU project) for statistical analysis. It uses GNU Scientific Library for mathematical operation and generation graph. You can open, analyze, edit and merge two or more database concurrently. The software supports over 1 billion cases and variables.

10. jHepWork


jHepWork is a platform data analysis, scientific computation and data visualization. It is written in Java and integrated with Python scripting language. It displays 2d and 3d plot for data sets for easy and efficient data analysis.



NLTK stands for Natural Language Toolkit. It provides a bunch of language processing tools such as data mining, data scraping, machine learning, sentiment analysis and more. It also guides the readers thought the fundamental of Python language, categorizing text, analyze linguistic structure and working with corpora.

8. Vowpal Wabbit

Vowpal Wabbit

Vowpal Wabbit is a machine learning project started at Yahoo research and continuing at Microsoft research to build scalable, fast and useful learning algorithm. It can exceed the throughput of any single machine network via parallel learning.

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KNIME is an open source data analytics, reporting and integration platform. It does the all 3 parts of data preprocessing i.e. extraction, transformation and loading. KNIME integrates different modules for data mining and machine learning through its modular data pipe-lining concept. Additional features can be added via plugins.

6. scikit-learn


scikit-learn provides a set of simple and efficient tools for data mining and analysis. It is open source as well as commercially usable software built on SciPy, NumPy and matplotlib. It supports preprocessing, classification, clustering, regression and dimensionality reduction.

5. Gephi


Gephi is an interactive visualization platform for complex systems, hierarchical graphs and all kinds of networks. The tool is based on NetBeans UI and packed with built-in 3d rendering engine. Also, you can customize the layouts, metrics, rendering presets via plugins.

4. R Project

R Project

R is a software programming language and software environment for statistical computing and graphics. It is widely used among data miners for analysis and building statistical software. Moreover, it also supports time-series analysis, classification, clustering, linear and non-linear modeling.

Read: 30+ Excellent Wireframing and Mockup Tools for Designers

3. Orange Data Mining


Orange is open source data visualization and analysis, perfect for Python developers. It includes components for machine learning, add-ons for text mining and bioinformatics. Till date, it supports bar charts, trees, scatter plots, heatmaps, data analysis tasks and have over 100 widgets.

2. Weka


Weka is set of machine learning algorithm (available under GPL v3 license) designed for solving real-world data mining problems. The algorithms can be applied directly to the database, or call from your Java code. It can be used in many different applications including data analysis, visualization, predictive modeling and more.

1. RapidMiner


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RapidMiner is a modern analytics platform that accelerates productivity from data rambling to predictive action. It works with any environment with any data from any source. You can embed your insights, take immediate action and deploy model in any way you want, within a few clicks.

Written by
Varun Kumar

I am a professional technology and business research analyst with more than a decade of experience in the field. My main areas of expertise include software technologies, business strategies, competitive analysis, and staying up-to-date with market trends.

I hold a Master's degree in computer science from GGSIPU University. If you'd like to learn more about my latest projects and insights, please don't hesitate to reach out to me via email at [email protected].

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  • Julien Damon says:

    Very Nice list of free tools, except that the full version of rapidminer is not free.

  • Sudhindra says:

    Awesome list of tools and I have now become a fan of your blog. Thanks a ton for the nice work 🙂

    • Varun Kumar says:

      Thank you Sudhindra for your kind words.

  • Many thanks for that list

  • BlueSky Statistic should be mentioned here.

    Most of the software is for developers, and on phD level requirement’s. Some are libraries also hard to setup and run. Lack of tuts and descriptions not for ordinary excel data analyst. Data input is cucumber some, also workflows are very different for the same alike analysis. RapidMiner praised for ease of use, not true at all. U can try to use it u see fast, where u were stopped and how.