Weka
Advertisement
Weka x64 v.3.7.9
Weka packahe provides a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, ...
Advertisement
WEKA Classification Algorithms v.1.8
A collection of plug-in algorithms for the WEKA machine learning workbench including artificial neural network (ANN) algorithms, and artificial immune system (AIS) algorithms.
Weka outlier v.b
weka outlier is an implementation of outlier detection algorithms for WEKA.CODB (Class Outliers: Distance-Based) Algorithm is the first algorithm developed using WEKA framework.
Weka.Net v.1.0
Weka.Net is a port to .Net of the Weka library.It use all the power of .net including some redisene of thelibrary to make more Object-Oriented.
Weka-Parallel v.1.0
Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.
WekatextToXML v.0.1.7
Weka is a complete and user-friendly data-mining environment that can be used for any research project. But Weka decision tree classifiers outputs the decision tree either as a Weka-syntaxed text tree or as a binary file (neither readable nor editable ...
WekaTransformer v.1.0.2142.32311
This tool acts as a preprocessor and transforms data from a database into arff format for weka data mining. Vertical to horizontal transformation for association analysis. The tool can use databases for which an OleDB adapter exists. Vb.Net for Win32.
KeplerWeka v.2.0.20101008
KeplerWeka adds the functionality of the open-source machine learning and data mining workbench WEKA to the free and open-source, scientific workflow application, Kepler.
PHMM4weka v.1.0
This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.
AlphaMiner v.2.0
Data mining capabilities from Xelopes and Weka have been incorporated in the first release. Versatile data mining functions offer powerful analytics to conduct industry specific analysis including customer profiling and clustering, product association ...
ELKI v.0.3
This separation makes ELKI unique among data mining frameworks like Weka or YALE and frameworks for index structures like GiST.
At the same time, ELKI is open to arbitrary data types, distance or similarity measures, or file formats.
The fundamental ...
RapidMiner Community Edition x64 v.5.3.005
FEATURES: TE 100% pure Java (runs on every major platform and operating system) TE KD processes are modeled as simple operator trees which is both intuitive and powerful TE operator trees or subtrees can be saved as building blocks for later re-use TE internal XML representation ensures standardized interchange format of data mining experiments TE simple scripting language allowing for automatic large-scale experiments TE multi-layered data view concept ensures efficient and transparent data handling Flexibility in using RapidMiner: TE graphical user interface (GUI) for interactive prototyping TE command line mode (batch mode) for automated large-scale applications TE Java API (application programming interface) to ease usage of RapidMiner from your own programs TE simple plugin and extension mechanisms, a broad variety of plugins already exists and you can easily add your own TE powerful plotting facility offering a large set of sophisticated high-dimensional visualization techniques for data and models TE more than 400 machine learning, evaluation, in- and output, pre- and post-processing, and visualization operators plus numerous meta optimization schemes TE machine learning library WEKA fully integrated (WEKA ...
RapidMiner Community Edition v.5.3.005
FEATURES: TE 100% pure Java (runs on every major platform and operating system) TE KD processes are modeled as simple operator trees which is both intuitive and powerful TE operator trees or subtrees can be saved as building blocks for later re-use TE internal XML representation ensures standardized interchange format of data mining experiments TE simple scripting language allowing for automatic large-scale experiments TE multi-layered data view concept ensures efficient and transparent data handling Flexibility in using RapidMiner: TE graphical user interface (GUI) for interactive prototyping TE command line mode (batch mode) for automated large-scale applications TE Java API (application programming interface) to ease usage of RapidMiner from your own programs TE simple plugin and extension mechanisms, a broad variety of plugins already exists and you can easily add your own TE powerful plotting facility offering a large set of sophisticated high-dimensional visualization techniques for data and models TE more than 400 machine learning, evaluation, in- and output, pre- and post-processing, and visualization operators plus numerous meta optimization schemes TE machine learning library WEKA fully integrated (WEKA ...
BioWeka v.0.6.1
alignments to the popular machine learning framework Weka.