Weka machine learning Wikipedia. Waikato Environment for Knowledge Analysis Weka is a suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License. DescriptioneditWeka pronounced to rhyme with Mecca contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non Java version of Weka was a TclTk front end to mostly third party modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a Makefile based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains,23 but the more recent fully Java based version Weka 3, for which development started in 1. Advantages of Weka include Free availability under the GNU General Public License. Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform. A comprehensive collection of data preprocessing and modeling techniques. Weka Software Windows' title='Weka Software Windows' />Ease of use due to its graphical user interfaces. Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Wekas techniques are predicated on the assumption that the data is available as one flat file or relation, where each data point is described by a fixed number of attributes normally, numeric or nominal attributes, but some other attribute types are also supported. Weka provides access to SQLdatabases using Java Database Connectivity and can process the result returned by a database query. Weka provides access to deep learning with Deeplearning. It is not capable of multi relational data mining, but there is separate software for converting a collection of linked database tables into a single table that is suitable for processing using Weka. Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling. NYU IT offers a wide variety of software in support of research, teaching, and class projects at the Student Technology Centers, the Digital Studio, Data Services. User interfaceseditWekas main user interface is the Explorer, but essentially the same functionality can be accessed through the component based Knowledge Flow interface and from the command line. There is also the Experimenter, which allows the systematic comparison of the predictive performance of Wekas machine learning algorithms on a collection of datasets. The Explorer interface features several panels providing access to the main components of the workbench The Preprocess panel has facilities for importing data from a database, a comma separated values CSV file, etc., and for preprocessing this data using a so called filtering algorithm. These filters can be used to transform the data e. The Classify panel enables applying classification and regression algorithms indiscriminately called classifiers in Weka to the resulting dataset, to estimate the accuracy of the resulting predictive model, and to visualize erroneous predictions, receiver operating characteristic ROC curves, etc., or the model itself if the model is amenable to visualization like, e. The Associate panel provides access to association rule learners that attempt to identify all important interrelationships between attributes in the data. The Cluster panel gives access to the clustering techniques in Weka, e. There is also an implementation of the expectation maximization algorithm for learning a mixture of normal distributions. The Select attributes panel provides algorithms for identifying the most predictive attributes in a dataset. The Visualize panel shows a scatter plot matrix, where individual scatter plots can be selected and enlarged, and analyzed further using various selection operators. Hardware And Software Parallelism In Computer Architecture Definition there. Extension packageseditIn version 3. Artikel Tentang Perkembangan Hardware Dan Software Internet. If your computer has a display that has a high pixel density, and you are using Windows, Wekas user interfaces may not be scaled appropriately and appear tiny. Java Development Kit also known as JDK contains the software and tools that you need to compile, debug, and run applets and applications that youve written using. Windows 8. 1 8in1 x86x64 enUS Murphy78, Free Download Windows 8. US Murphy78, Free Windows 8. US Murphy78 Full, Windows 8. Machine learning software to solve data mining problems. Home weka37windowsx64. Name Modified. Thanks for helping keep SourceForge clean. Download Weka for free. Machine learning software to solve data mining problems. Weka is a collection of machine learning algorithms for solving realworld data. Weka Software Windows' title='Weka Software Windows' />Some functionality that used to be included with Weka prior to this version has since been moved into such extension packages, but this change also makes it easier for others to contribute extensions to Weka and to maintain the software, as this modular architecture allows independent updates of the Weka core and individual extensions. HistoryeditIn 1. University of Waikato in New Zealand began development of the original version of Weka, which became a mix of TclTk, C, and Makefiles. In 1. 99. 7, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms. In 2. Weka received the SIGKDD Data Mining and Knowledge Discovery Service Award. View Memory Mapped Files. In 2. Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence. It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. Related toolseditSee alsoeditReferenceseditExternal linksedit.