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.
Table of Contents
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 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.
17. MALLET
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.
16. CLUTO
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
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 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.
9. NLTK
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 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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7. KNIME
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 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.
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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.
Very Nice list of free tools, except that the full version of rapidminer is not free.
Awesome list of tools and I have now become a fan of your blog. Thanks a ton for the nice work 🙂
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.
What nobody tells you is performance of such app. Python based solutions are toy solutions despite many would not agree. Why ? Well python is not parallel processing language, its a serial execution language. Using python for ML and generative NN’s is like speed of snail execution. Orange & Scikit (python, anaconda total mess) does not use any of hardware acceleration available and using python on CUDA is mission impossible, available only if one code it for him self. Closest to achieve best performance on your hardware is RapidMiner (H2O) and MatLab. For others do not know. But in general any Java based and C++ based app uses parallelism by default. So that being said, data mining without any of available hardware acceleration is way to much slow thus useless. First ARM based CPU solution promise a lot as being TRUE parallels solutions but there’s a catch. If hardware vendors do not agree about CPU+GPU hybridization by default (we will have similar problems as end user as we have now, one can kill him self to achieve parallel processing on existed hardware) on such hardware solutions like Intel, Nvidia, AMD, also Microsoft & others we we screwed up again. We are already screwed as end user due we buy/pay for performance hardware on which we cant utilize promised on paper hardware power on which we decide to buy such hardware ! Its like buying a race car which can go only 1/4 of promised speed. Pure waist of money. As of python lovers sure there are advantages but until python isn’t using hardware acceleration for serious work it is useless. I wonder how developers would decide to run python on ARM’s. Serial executions languages suitable only for where parallelism does not matter only.