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Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato , New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques".
Auto-WEKA is an automated machine learning system based on Weka by Chris Thornton, Frank Hutter, Holger H. Hoos and Kevin Leyton-Brown. [1] An extended version was published as Auto-WEKA 2.0. [2] Auto-WEKA was named the first prominent AutoML system in a neutral comparison study. [3] It received the test-of-time award of the SIGKDD conference ...
RapidMiner – Data mining software written in Java, fully integrating Weka, featuring 350+ operators for preprocessing, machine learning, visualization, etc. – the prior version is available as open-source; Scriptella ETL – ETL (Extract-Transform-Load) and script execution tool. Supports integration with J2EE and Spring.
Weka is part of machine learning curriculum in many universities. It is also among the few openly available toolkits to test machine learning algorithms (bayes, j48, ZeroR, OneR) on sample data sets, create models and apply the learnt models on new test sets of data.
Weka contains (as an optional package in latest versions) a basic implementation of DBSCAN that runs in quadratic time and linear memory. linfa includes an implementation of the DBSCAN for the rust programming language. Julia includes an implementation of DBSCAN in the Julia Statistics's Clustering.jl package.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Download QR code; Print/export Download as PDF; Printable version; In other projects Wikidata item; ... Weka (software) Wolfram Mathematica; World Programming System; X.
The third generation of Feature Selection Toolbox (FST3) was a library without user interface, written to be more efficient and versatile than the original FST1. [3]FST3 supports several standard data mining tasks, more specifically, data preprocessing and classification, but its main focus is on feature selection.