Mallet is a java machine learning toolkit for textual document.
The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection. The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows, released under GPLv3.Įnvironment for Developing KDD-Applications Supported by Index-Structure (ELKI) is an open source (AGPLv3) data mining software written in Java. MEKA is based on the WEKA Machine Learning Toolkit. This different from the ‘standard’ case which involves only a single target variable. In multi-label classification, we want to predict multiple output variables for each input instance. The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Massive Online Analysis (MOA) is a popular open source framework for data stream mining, with a very active growing community. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
The algorithms can either be applied directly to a dataset or called from your own Java code. Weka has a collection of machine learning algorithms for data mining tasks.
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