Software
We've compiled a list of 10 free and paid alternatives to KEEL. The primary competitors include KNIME Analytics Platform, Orange Data Mining. In addition to these, users also draw comparisons between KEEL and RapidMiner, Neural Designer, WEKA. Also you can look at other similar options here: Education and Reference Software.
We've compiled a list of 10 free and paid alternatives to KEEL. The primary competitors include KNIME Analytics Platform, Orange Data Mining. In addition to these, users also draw comparisons between KEEL and RapidMiner, Neural Designer, WEKA. Also you can look at other similar options here: Education and Reference Software.
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KNIME Analytics Platform is an open data analytics and visualization platform that allows you to integrate, process, analyze, and visualize data. This platform simplifies the creation of complex analytical solutions and includes a wide range of tool…
53
Open-source software for visual data analysis and machine learning. It is developed in the Python programming language and provides an intuitive graphical user interface. Orange provides a wide range of tools for working with data, including visuali…
25
RapidMiner makes data science teams more productive through an open source platform for data prep, machine learning, and model deployment.
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Weka is a collection of machine learning algorithms for data mining tasks; with its own GUI.
15
Apache Mahout is an Apache project to produce free implementations of distributed or otherwise...
13
SIMON is powerful, flexible, open-source and easy to use machine learning knowledge discovery...
10
Boost supply chain efficiency with advanced analytics and machine learning. Automate real-time data analysis for precise demand forecasting and inventory optimization.
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KEEL is an open source (GPLv3) Java software tool to assess evolutionary algorithms for Data Mining...
KEEL is an open source (GPLv3) Java software tool to assess evolutionary algorithms for Data Mining...
Windows
Linux
Mac
KEEL is an open source (GPLv3) Java software tool to assess evolutionary algorithms for Data Mining problems including regression, classification, clustering, pattern mining and so on. It contains a big collection of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, etc.), Computational Intelligence based learning algorithms, including evolutionary rule learning algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...), and hybrid models such as genetic fuzzy systems, evolutionary neural networks, etc. It allows us to perform a complete analysis of any learning model in comparison to existing ones, including a statistical test module for comparison. Moreover, KEEL has been designed with a double goal: research and educational.
Data Mining
Clustering
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