data mining tools

Data mining is the process of finding patterns from the large set of data. Data mining is also called as knowledge discovery in databases. Every data collected has its own purpose, just collecting and storing the data won’t help us achieve things. Hence it has to be processed so that it will be useful and serve its purpose. Data mining helps companies to compete in the market. This strategy helps global leads to launch what their customers love based on their past information. Data mining is dealing with the enormous amount of data, therefore it requires distinctive machines with appropriate tools/software to make a better result.

Analyzing customer preferences, customers transactional models, fraud detection, predicting future trends, the market-based analysis is some of the advantages of data mining.

This article showcases top 10 data mining tools based on their usage and use cases.



  • Weka is a Java-based open source data mining tool, under GNU General Public Licence.
  • Weka supports Linux, Mac and Windows platforms. Weka has been built for data mining and machine learning.
  • Weka tool supports data preprocessing, classification, regression, clustering, association rules, visualization. Weka has both Command Line and GUI approach.
  • Command Line interface is advised for immense data sets because the GUI approach will cause performance issues.WEKA supports various file formats.
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  • Rapid Miner has two different releases a FOSS(Free and Open Source) and a commercial edition.
  • This tool also provides support for Data preparation, machine learning, deep learning, text mining and predictive analytics.
  • Rapid miner is a “Lightning fast data science platform” as by rapid miner team
  • Rapid Miner free edition is limited to 1 logical processor and 10,000 data rows.
  • Rapid Miner can be integrated with R and Python for rapid prototyping.



  • Orange is a python library having rich scripts for data mining and machine learning purposes. Python-based data scientists are familiar with Orange’s algorithm.
  • The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, visualize, classify, regression, evaluate and unsupervised). Additional functionalities are available as add-ons (bioinformatics, data fusion, and text-mining).
  • Orange has Easy-to-use User Interface (UI) and loads of online tutorials for help.
  • Orange supports Mac, Linux and Windows platform.



  • Knime is an opensource data integration, data analytics tool also serves its purpose for reporting and modelling.
  • Knime is written in Java and built upon eclipse, hence uses JDBC model for data preprocessing and blending data sources.
  • Knime has a free and a commercial version.
  • Knime has plenty of eclipse add-ons to provide functionalities like text and image mining.
  • Also owning a larger community for support.
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  • DataMelt is a software ecosystem for numeric calculations, statistics and data analysis.
  • DataMelt / DMelt is also used in many areas other than data mining such as natural sciences, engineering, modelling and analysis of financial markets.
  • DataMelt supports languages like Java, Python/Jython, Groovy and can create data visualizations or outputs in SVG, PDF, EPS formats.



  • According to the official root, Apache Mahout project’s goal is to build a highly scalable machine learning Library focused fundamentally in the areas of collaborative filtering, clustering, and classification.
  • Apache Mahout uses Apache Hadoop platform for implementation. Mahout is used by tech giants like Adobe, AOL, Drupal, and Twitter and also in scholastic sides.



  • Elki is a tool to perform knowledge discovery in databased.ELKI – Environment for deveLoping KDD-Applications supported by Index-structures.ELKI is an opensource platform built with Java. ELKI’s algorithm is focused on cluster analysis and outlier detection.
  • ELKI has been preferred for its high performance and scalability, offers good performance index structures like R– tree.
  • Due to separate algorithms for data mining and data management tasks makes ELKI unique from the market when compared with WEKA and Rapid Miner.



  • KEEL  stands for Knowledge Extraction based Evolutionary Learning, an open source software built using Java.
  • KEEL has a simple GUI based on the data flow to design experiments with different datasets and computational intelligence algorithms.
  • It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques, computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments
  • KEEL provides a complete analysis of new computational intelligence proposals in comparison to existing ones.
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  • Massive Online Analysis (MOA) is written in Java
  • MOA, a framework for data stream mining.
  • MOA has tools for evaluation and collection Machine Learning algorithms ( classification, regression, clustering, outlier detection concept drift detection and recommender systems).
  • The strength of MOA can be extended with new algorithms and evaluating measures.
  • The goal is to provide a benchmark suite for the stream mining community.



  • Rattle is a GUI based data mining and analytics tool using R.
  • Results of Rattle can be readily modelled and builds both supervised and unsupervised Machine Learning models from the data.
  • All GUI actions were captured as R-scripts and executed in R independently from the Rattle interface



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