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This article mainly explains "what are the excellent open source tools for data mining in machine learning". The content in the article is simple and clear, and it is easy to learn and understand. let's study and learn what are the excellent open source tools for data mining in machine learning.

Apache Mahout

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

Apache Mahout has many advantages, such as allowing applications to analyze large data sets in a faster way, and support for mathematical expressions Scala DSL; supports multiple distributed backends, including Apache Spark; 's modular native solver for CPU/GPU/CUDA acceleration.

DataMelt

DataMelt or DMelt is open source software for numerical calculation, mathematics, statistics, symbolic calculation, data analysis and data visualization. The platform is a combination of various scripting languages such as Python,Ruby,Groovy, as well as other Java software packages.

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

ELKI

The environment for developing KDD applications supported by Index-Structures or ELKI is open source data mining software written in the Java language. The platform can study algorithms, with emphasis on unsupervised methods in clustering analysis and outlier detection.

It provides data index structures, such as R* trees, which can significantly improve performance; it is convenient for researchers and students in this field to expand; and ELKI provides a large number of algorithms that can be highly parameterized.

Knime

KNIME Analytics Platform, written in Java and based on Eclipse, is open source software for carrying data science tasks. It is a multilingual software development environment, including an integrated development environment (IDE) and an extensible plug-in system. Knime is a free data analysis, reporting and integration platform.

It allows users to choose from more than 2000 nodes to build workflows, and allows visual workflows to be created using an intuitive drag-and-drop graphical interface without programming.

Orange

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

Orange has the function of interactive data visualization and can also perform simple data analysis; it includes interactive data search and can be quickly and qualitatively analyzed through clear visualization.

Rattle

Rattle, written in R language, is a popular open source GUI for data mining that displays statistics and visual summaries of data. It can transform data so that it can be modeled. It builds unsupervised and supervised machine learning models based on data, graphically displays the performance of the model, and scores new data sets for deployment into production.

Rattle demonstrates the capabilities of R Statistics software through GUI, which provides considerable data mining capabilities; all interactions through the graphical user interface are captured as R scripts and can be easily executed in R independent of the Rattle interface; this tool can be used to learn and develop R's skills, and then build the initial model in Rattle.

Scikit-learn

Scikit-learn is a popular Python library for data analysis and data mining. It is based on SciPy,Numpy and Matplotlib. The main functions of scikit learning are classification, regression, clustering, dimension reduction, model selection and data preprocessing.

Scikit-learn includes simple and effective tools for predictive data analysis; it provides popular models, including dimensionality reduction, cross-validation integration methods, parameter tuning, and so on.

Weka

Weka or Waikato knowledge analysis environment is a popular open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or Java API. It is a collection of machine learning algorithms used to solve practical data mining problems. It is written in Java and can be run on almost any platform.

Weka contains a number of built-in tools for standard machine learning tasks that provide transparent access to well-known toolkits such as scikit-learn,R and Deeplearning4j.

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