It is rightfully said that data is money in today’s world. Along with the transition to an app-based world comes the exponential growth of data. However, most of the data is unstructured and hence it takes a process and method to extract useful information from the data and transform it into understandable and usable form.
Data mining or “Knowledge Discovery in Databases” is the process of discovering patterns in large data sets with artificial intelligence, machine learning, statistics, and database systems.
Free data mining tools ranges from complete model development environments such as Knime and Orange, to a variety of libraries written in Java, C++ and most often in Python. There are four kinds of tasks that are normally involve in Data mining:
- Classification: the task of generalizing familiar structure to employ to new data
- Clustering: the task of finding groups and structures in the data that are in some way or another the same, without using noted structures in the data.
- Association rule learning: Looks for relationships between variables.
- Regression: Aims to find a function that models the data with the slightest error.
Listed below are our list of 19 free software tools for Data Mining.
Data Mining Tools
1. Rapid Miner
Rapid Miner, formerly called YALE (Yet another Learning Environment), is an environment for machine learning and data mining experiments that is utilized for both research and real-world data-mining tasks.
It is unquestionably the world-leading open-source system for data mining. Written in the Java Programming language, this tool offers advanced analytics through template-based frameworks.
It enables experiments to be made up of a huge number of arbitrarily nest able operators, which are detailed in XML files and are made with the graphical user interface of Rapid Miner. The best thing is that users do not need to write codes. It already has many templates and other tools that lets us analyze the data easily.
2. IBM SPSS Modeler
IBM SPSS Modeler tool workbench is best for working on large-scale projects like textual analytics, and its visual interface is extremely valuable. It allows you to generate a variety of data mining algorithms with no programming.
It can also be used for anomaly detection, Bayesian networks, CARMA, Cox regression and basic neural networks that use multilayer perceptron with back-propagation learning. Not for the faint of heart.
3. Oracle Data Mining
Another big hitter in the data mining sphere is Oracle. As part of their Advanced Analytics Database option, Oracle data mining allows its users to discover insights, make predictions and leverage their Oracle data. You can build models to discover customer behavior target best customers and develop profiles.
The Oracle Data Miner GUI enables data analysts, business analysts and data scientists to work with data inside a database using a rather elegant drag and drop solution. It can also create SQL and PL/SQL scripts for automation, scheduling and deployment throughout the enterprise.
Teradata recognizes the fact that, although big data is awesome, if you don’t actually know how to analyze and use it, it’s worthless. Imagine having millions upon millions of data points without the skills to query them. That’s where Teradata comes in. They provide end-to-end solutions and services in data warehousing, big data and analytics and marketing applications.
Teradata also offers a whole host of services including implementation, business consulting, training and support.
See Also: 36 Fascinating Facts about Cloud Computing
5. Framed Data
It’s a fully managed solution which means you don’t need to do anything but sit back and wait for insights. Framed Data takes data from businesses and turns it into actionable insights and decisions. They train, optimize, and store product ionized models in their cloud and provide predictions through an API, eliminating infrastructure overhead.
They provide dashboards and scenario analysis tools that tell you which company levers are driving metrics you care about.
Kaggle is the world’s largest data science community. Companies and researchers post their data and statisticians and data miners from all over the world compete to produce the best models.
Kaggle is a platform for data science competitions. It help you solve difficult problems, recruit strong teams, and amplify the power of your data science talent.
WEKA is a very sophisticated data mining tool. It shows you various relationships between the data sets, clusters, predictive modelling, visualization etc. There are a number of classifiers you can apply to get more insight into the data.
Rattle stands for the R Analytical Tool to Learn Easily. It presents statistical and visual summaries of data, transforms data into forms that can be readily modelled, builds both unsupervised and supervised models from the data, presents the performance of models graphically, and scores new datasets.
It is a free and open source data mining toolkit written in the statistical language R using the Gnome graphical interface. It runs under GNU/Linux, Macintosh OS X, and MS/Windows.
Konstanz Information Miner is a user friendly, intelligible and comprehensive open-source data integration, processing, analysis and exploration platform. It has a graphical user interface which helps users to easily connect the nodes for data processing.
KNIME also integrates various components for machine learning and data mining through its modular data pipelining concept and has caught the eye of business intelligence and financial data analysis.
As a free and open source language, Python is most often compared to R for ease of use. Unlike R, Python’s learning curve tends to be so short it’s become legendary. Many users find that they can start building data sets and doing extremely complex affinity analysis in minutes.
The most common business-use case-data visualizations are straightforward as long as you are comfortable with basic programming concepts like variables, data types, functions, conditionals and loops.
Orange is a component based data mining and machine learning software suite written in Python Language. It is an Open Source data visualization and analysis for novice and experts. Data mining can be done through visual programming or Python scripting.
It is also packed with features for data analytics, different visualizations, from scatterplots, bar charts, trees, to dendrograms, networks and heat maps.
12. SAS Data Mining
Discover data set patterns using SAS Data Mining commercial software. Its descriptive and predictive modelling provides insights for better understanding of the data. They offer an easy to use GUI.
They have automated tools from data processing, clustering to the end where you can find best results for taking right decisions. Being a commercial software it also includes advanced tools like Scalable processing, automation, intensive algorithms, modelling, data visualization and exploration etc.
13. Apache Mahout
Apache Mahout is a project of the Apache Software Foundation to produce free implementations of distributed or otherwise scalable machine learning algorithms focused primarily in the areas of collaborative filtering, clustering and classification.
Apache Mahout supports mainly three use cases: Recommendation mining takes users’ behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents.
Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabeled documents to the (hopefully) correct category.
PSPP is a program for statistical analysis of sampled data. It has a graphical user interface and conventional command-line interface. It is written in C, uses GNU Scientific Library for its mathematical routines, and plot UTILS for generating graphs.
It is a Free replacement for the proprietary program SPSS (from IBM) predict with confidence what will happen next so that you can make smarter decisions, solve problems and improve outcomes.
jHepWork is a free and open-source data-analysis framework that is created as an attempt to make a data-analysis environment using open-source packages with a comprehensible user interface and to create a tool competitive to commercial programs.
JHepWork shows interactive 2D and 3D plots for data sets for better analysis. There are numerical scientific libraries and mathematical functions implemented in Java. jHepWork is based on a high-level programming language Jython, but Java coding can also be used to call jHepWork numerical and graphical libraries.
16. R programming Language
There’s no mystery why R is the superstar of free data mining tools on this list. It’s free, open source and easy to pick up for people with little to no programming experience. There are literally thousands of libraries that can be incorporated into the R environment making it a powerful data mining environment.
It’s a free software programming language and software environment for statistical computing and graphics. The R language is widely used among data miners for developing statistical software and data analysis. Ease of use and extensibility has raised R’s popularity substantially in recent years.
Pentaho provides a comprehensive platform for data integration, business analytics and big data. With this commercial tool you can easily blend data from any source. Get insights into your business data and make more accurate information driven decisions for future.
TANAGRA is a data mining software for academic and research purposes. There are tools for exploratory data analysis, statistical learning, machine learning and databases area.
Tanagra contains some supervised learning but also other paradigms such as clustering, factorial analysis, parametric and non-parametric statistics, association rule, feature selection and construction algorithms.
Natural Language Toolkit, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the python language.
It provides a pool of language processing tools including data mining, machine learning, data scrapping, sentiment analysis and other various language processing tasks. Build python programs to deal with human language data.