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8 Best Open-Source Tools for Data Mining

One of the popular terms in machine learning techniques is data mining. It is the process of extracting hidden or previously unknown and potentially useful information from the large sets of data. The outcome can be for analysing and achieving meaningful insights for the development of an organisation.  

In this article, we list down the eight best open-source data mining tools one must know.

(The list is in alphabetical order)

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1| Apache Mahout

Apache Mahout is a popular distributed linear algebra framework. The framework is a mathematically expressive Scala DSL which is designed to let statisticians and data scientists implement their algorithms in a faster manner. It builds an environment for quickly creating scalable and performance-driven machine learning applications.

Some of the features are-


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  • Mathematically Expressive Scala DSL
  • Support for Multiple Distributed Backends (including Apache Spark)
  • Modular Native Solvers for CPU/GPU/CUDA Acceleration
  • It allows applications to analyse large datasets in a faster manner

Know more here.

2| DataMelt

DataMelt or DMelt is open-source software for numeric computation, mathematics, statistics, symbolic calculations, data analysis and data visualisation. The platform is a combination of various scripting languages such as Python, Ruby, Groovy, among others with several Java packages.

Some of the features are-

  • DMelt is a computational platform and can be used with different programming languages on various operating systems
  • DataMelt can be used with several scripting languages for the Java platform, such as Jython (Python programming language), Groovy, JRuby (Ruby programming language) and BeanShell.
  • It creates high-quality vector-graphics images (SVG, EPS, PDF, etc.) that can be included in LaTeX and other text-processing systems.

Know more here.

3| ELKI

Environment for Developing KDD-Applications Supported by Index-Structures or ELKI is an open-source data mining software written in Java language. This platform aims to research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. 

Some of the features are-

  • It provides data index structures such as the R*-tree that can provide significant performance gains
  • The platform is designed to be easy to extend for researchers and students in this domain
  • ELKI provides an extensive collection of highly parameterisable algorithms

Know more here.

4| Knime

Written in Java and based on Eclipse, KNIME Analytics Platform is open-source software for carrying tasks in data science. It is a multi-language software development environment and comprises an integrated development environment (IDE) and an extensible plug-in system. Knime is a free data analytics, reporting and integration platform which creates intuitive and continuously integrating new developments.

Some of the features are-

  • It allows you to choose from over 2000 nodes to build your workflow
  • It allows to create visual workflows with an intuitive, drag and drop style graphical interface, without the need for coding

Know more here.  

5| Orange

Orange is an open-source, component-based data mining software for machine learning and data visualisation. It includes a range of data visualisation, exploration, preprocessing and modelling techniques and can be used as a module for the Python programming language. 

Some of the features are-

  • Orange has interactive data visualisation and can also perform simple data analysis
  • It includes interactive data exploration for rapid qualitative analysis with clean visualisation

Know more here.

6| Rattle

Written in R language, Rattle is a popular open-source GUI for data mining that presents statistical and visual summaries of data. It transforms data so that it can be readily modelled. It builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically, and scores new datasets for deployment into production.

Some of the features are-

  • It provides considerable data mining functionality by exposing the power of the R Statistical Software through a GUI
  • All of the interactions through the graphical user interface are captured as an R script that can be readily executed in R independently of the Rattle interface
  • The tool can be used to learn and develop skills in R and then to build initial models in Rattle

Know more here.

7| scikit-learn

scikit-learn is a popular Python library for data analysis and data mining that is built on top of SciPy, Numpy and Matplotlib. The primary functions of scikit-learn are divided into classification, regression, clustering, dimensionality reduction, model selection, as well as data preprocessing. 

Some of the features are-

  • Scikit-learn include simple and efficient tools for predictive data analysis
  • It provides popular models including dimensionality reduction, cross-validation ensemble methods, manifold learning, parameter tuning and much more. 

Know more here.

8| Weka

WEKA or Waikato Environment for Knowledge Analysis is a popular open-source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform.

Some of the features are-

  • WEKA contains a plethora of built-in tools for standard machine learning tasks
  • It provides transparent access to well-known toolboxes such as scikit-learn, R as well as Deeplearning4j

Know more here.

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Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

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