Search Results for "weka c#"

JAETL - Just Another ETL tool is a tiny and fast ETL tool to develop data warehouse. JAETL allows to Extract data from ARFF (Weka), CSV, and SQL, Transform the data with join, replace missing values, remove duplicates, mapping filtering, variable selection, and Load the data into SQL server and export to CSV and ARFF.

...While JASP offers more statistical features, DSTK tends to be a broad solution workbench, including text analysis and predictive analytics features. Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses.

Excel to Arff converter. You can easily convert the excel datas will be used data mining process to arff file format and then easily analyze your datas and results using WEKA Data Mining Utility.

LEET (Large Experiment and Evaluation Tool) is a front-end software utility for WEKA that simplifies large-scale experiment and evaluation of algorithms and datasets in the classification context.

Weka.Net is a port to .Net of the Weka library. It use all the power of .net including some redisene of the library to make more Object-Oriented.

The WEKA port to .NET 2.0 Platform, will be useful for Data Mining in .NET Environment

MLP is a library containing a framework for the development of Machine Learning algorithms as well as a set of standard ML algorithms ready to be used. It is similar in the spirit to the weka project, but it is written in C++.

Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.

Weka++ is a collection of machine learning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.

ktdata is a C++ library for accessing tabular data, like from CSV files. Its goals are: 1. object design 2. portability (Linux and Windows support at least) 3. high performance 4. support for common data file formats, like CSV, ARFF (Weka), etc.