Search results
Results from the WOW.Com Content Network
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".
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.
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 ...
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.
Article should not be deleted. It needs expansion. 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: Pentaho Data Mining used the Waikato Environment for Knowledge Analysis to search data for patterns. Weka consists of machine learning algorithms for a broad set of data mining tasks. [5] It contains functions for data processing, regression analysis, classification methods, cluster analysis, and visualization.
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.
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.