WEKA
WEKA is a set of powerful data mining tools that run on Java.
- WEKA is a set of powerful, Java-based tools built for data mining.
- WEKA has both a graphical user interface (GUI) and a command-line interface (CLI) that can be used to access all the available funtionality of the software.
- There is support for Hadoop and Spark through wrappers.
- Additional information regarding these wrappers is available on the WEKA site.
- Since WEKA runs on Java, it can be used on any platform that supports Java, including Windows, Mac, Linux and BSD.
- WEKA is open source software. It uses the GNU General Public License (GPL) version 2.0 or 3.0, depending on the version of WEKA installed. This means that the software's source code is available for review and that the software may be used freely and for any purpose.
- There are free courses available online teaching how to use WEKA for machine learning and data mining.
- WEKA runs on Java, which is memory intensive.
- When you load a dataset into the WEKA Explorer (the graphical user interface),
- For very large datasets,
- Fortunately, this can be circumvented by using the Command-line interface (CLI) for loading and processing the large dataset.
- While WEKA is open source, using it for commercial applications which require distributing the WEKA code as part of the application may require you to purchase licenses from one or more corporate entities.
- The host for the WEKA WIKI data (wikispaces) is shutting down, and information on where this data will reside in the long term is not yet available.
WEKA is an open source project which contains algorithms and tools that can be used for data mining. There are a variety of tools available in WEKA that can perform all the tasks necessary to crunch through large volumes of data. WEKA was developed by and is supported by the University of Waikato in New Zealand. There are Massive Open Online Courses (MOOC) for WEKA that enable you to learn how to use the WEKA tools to analyze your data. There are beginning, intermediate and advanced data analysis courses available. Each is taught by a professor from the Department of Computer Science from the University of Waikato. You can access your dataset via JDBC, CSV or a variety of other connection methodologies. You load the data into WEKA, train your data model and process your data. In addition to the ability to develop Java solutions using the WEKA tools, you can also access WEKA functionality from .NET, Python and Groovy. There are often additional steps required, but the core functionality is available to these other development tools.
Alternatives to WEKA
Azure Machine Learning
DatumBox
Root
ShARk
PredictionIO
