• Sep 28, 2009 Subject: Re: Wekalist Could not find the main class (WEKA 3.6) Hi MikeI don't know how to fix the classpath. I saw a link to instructions on how to do this in a thread from January 2007, but.
• Jul 31, 2016 weka-3-7-4jre.exe. Alternative software FREE. Virtual Machine Recovery Tool. It temporarily removes a host, cluster.

WEKA --- A Starter's Guide

Another novelty is a dedicated distribution for Linux, which also includes the same Zulu JVM in a sub folder. The.zip files holding the new Linux distributions of WEKA 3.8.4/3.9.4 contain a script with the name weka.sh that starts WEKA using the Zulu JVM. Weka 3 Next, depending on the kind of ML model that you are trying to develop you would select one of the options such as Classify, Cluster, or Associate. The Attributes Selection allows the automatic selection of features to create a reduced dataset. Note that under each category, WEKA provides the implementation of several algorithms. Weka for Windows PC – Learn how to download and install the Weka 3.9.3 (32-bit) application on your Windows 10/8/7 PC for free. Weka is a collection of machine learning algorithms for data mining tasks.

(new version 3.4 installed Jan 22, 2004)

You are about to learn WEKA, a practical machine learning tools package to help you in your following lab assignments. 'WEKA' stands for the Waikato Environment for Knowledge Analysis, which was developed at the University of Waikato in New Zealand. WEKA is extensible and has become a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost every platform.

WEKA is easy to use and to be applied at several different levels. You can access the WEKA class library from your own Java program, and implement new machine learning algorithms.

There are three major implemented schemes in WEKA. (1) Implemented schemes for classification. (2) Implemented schemes for numeric prediction. (3) Implemented 'meta-schemes' .

Besides actual learning schemes, WEKA also contains a large variety of tools that can be used for pre-processing datasets, so that you can focus on your algorithm without considering too much details as reading the data from files, implementing filtering algorithm and providing code to evaluate the results.

We have installed one copy of WEKA package in our CS573 course account. You don't have to install your own copy, and by doing the following steps, you will be able to run it:

1) Log into your CS account.

2) Create a directory called weka using:

> mkdir weka

> cd weka

3) Create a symbolic link to our installed weka pack:

> ln -s /home/course/cs573x/weka-3-4/weka.jar

4) You are able to run weka now:

> java -jar weka.jar

*note: this will only work on Linux or Solaris workstations, as this will run GUI version of WEKA

5) Please make the following two links to enable you to access the source code of weka and some sample datasets.

> ln -s /home/course/cs573x/weka-3-4/weka

> ln -s /home/course/cs573x/weka-3-4/data

The following step is to set the CLASSPATH environment variable prior to use the command line. You need to indicate the location of the weka.jar file.

1) For sh, ksh and bash users:

Please add 'export CLASSPATH=/home/course/cs573x/weka-3-4/weka.jar:$CLASSPATH' into your shell configuration profile.

2) For csh and tcsh users:

Please add 'setenv CLASSPATH /home/course/cs573x/weka-3-4/weka.jar' into your shell configuration profile.

3) To test if it is set correctly:

Type 'java weka.classifiers.trees.j48.J48 ' (note the change from previous versions, in case you are familiar with any), it should display a list of all learning options for J48. If it displays an exception error message, then you will check if you set the environment variable correctly. Please make sure that you also set the correct path to Java, so that the system can locate and run Java.

Now, the installation is done! You can run WEKA either in command line or in graphic user interface. Remember only in Linux or a Solaris workstation can you run GUI, telnet won't work.

• How to install and run WEKA on your home computer

If you have a home computer and would like to install WEKA by yourself, please check the following:

There are two stable versions of WEKA. Either you can download the self-extraction executable version that includes the Java Virtual Machine 1.4 (weka-3-4jre.exe; 19,543,851 bytes), or the self-extracting executable without Java VM (weka-3-4.exe; 6,467,165 bytes). This new version comes with the GUI, which provides the user with more flexibility than the command line.

After extracting the files, you will need to set your classpath variable to a complete path to weka.jar (suppose you extracted WEKA to C:Weka, then set your classpath variable to C:Wekaweka.jar, ie add 'C:Wekaweka.jar;' to the list of values that environment variable Path can take when working in Windows)

If you don't have administrator privileges, you can still install WEKA. For that, download the jar archive (weka-3-4.jar; 6,322,417 bytes). Make sure that the Java J2SE 1.4 (download from SUN) is installed on your system (which includes the jar utility). Then open a command line console, change into the directory containing weka-3-4.jar, and enter

jar -xvf weka-3-4.jar

This will create a new directory called weka-3-4. To un-jar (install) the source code, position yourself in the recently created weka-3-4 directory and type

jar -xvf weka-src.jar

Which will create a new directory weka containing the source code. Since WEKA is open source software issued under the GNU General Public License, you can use and modify the source code as you like.

NOTE: It seems that Windows will not set up your CLASSPATH properly if any of the WEKA directories contains spaces. Therefore, installing Weka in the Program Files folder is not a good idea.

From your weka-3-4 directory, you will find:

• A jarfile containing the classes only
• A jarfile containing the complete source code
• The tutorial and README
• The API documentation
• Some example datasets

It is very helpful to learn more about the structure of WEKA before you use it in your program. You should read the tutorial and README files carefully, and explore the hierarchical API documentation to get familiar with the Packages, Interfaces and Classes. The most detailed and up-to-date information could be found in the online documentation (stable book version or GUI version) on WEKA Web Site. This documentation is an online html-based API documentation generated directly from the source code using the Javadoc utility.

The tutorial is Chapter 8 of the book Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations.From this tutorials, you will learn how to prepare your dataset in ARFF format; how to use filters to preprocess a dataset; how to apply learning scheme to your data set in command line; how to access the classes from your own code, and how to write new learning schemes according to your purpose. It also includes examples and sample codes that you can try to get hands-on experiences.

The Weka Wiki page has a lot of documentation and guides on installation/usage pages.

You can click here to download several tutorial for the experiment environment in the GUI version of Weka (written by David Scuse), and here is a working demonstration of using WEKA Knowledge Explorer.

This page is written by Jun Zhang (updated by Oksana Yakhnenko). If you have any questions or comments about the installation and usage of WEKA, please email: rjordan@cs.iastate.edu

Waikato Environment for Knowledge Analysis (Weka), developed at the University of Waikato, New Zealand, is free software licensed under the GNU General Public License, and the companion software to the book 'Data Mining: Practical Machine Learning Tools and Techniques'.^[1]

Description[edit]

Weka 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.^[1] The original non-Java version of Weka was a Tcl/Tk 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,^[2][3] but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. 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.
• Ease of use due to its graphical user interfaces.

Weka 3

Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization, and feature selection. All of Weka's 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 Deeplearning4j.^[4] 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.^[5] Another important area that is currently not covered by the algorithms included in the Weka distribution is sequence modeling.

Extension packages[edit]

In version 3.7.2, a package manager was added to allow the easier installation of extension packages.^[6]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.

History[edit]

• In 1993, the University of Waikato in New Zealand began development of the original version of Weka, which became a mix of Tcl/Tk, C, and Makefiles.
• In 1997, the decision was made to redevelop Weka from scratch in Java, including implementations of modeling algorithms.^[7]
• In 2005, Weka received the SIGKDD Data Mining and Knowledge Discovery Service Award.^[8][9]
• In 2006, Pentaho Corporation acquired an exclusive licence to use Weka for business intelligence.^[10] It forms the data mining and predictive analytics component of the Pentaho business intelligence suite. Pentaho has since been acquired by Hitachi Vantara, and Weka now underpins the PMI (Plugin for Machine Intelligence) open source component.^[11]

Related tools[edit]

Weka 3.7

• Auto-WEKA is an automated machine learning system for Weka.^[12]
• Environment for DeveLoping KDD-Applications Supported by Index-Structures (ELKI) is a similar project to Weka with a focus on cluster analysis, i.e., unsupervised methods.
• H2O.ai is an open source data science and machine learning platform
• KNIME is a machine learning and data mining software implemented in Java.
• Massive Online Analysis (MOA) is an open-source project for large scale mining of data streams, also developed at the University of Waikato in New Zealand.
• Neural Designer is a data mining software based on deep learning techniques written in C++.
• Orange is a similar open-source project for data mining, machine learning and visualization based on scikit-learn.
• RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka.
• Scikit-learn is a popular machine learning library in Python.

See also[edit]

References[edit]

1. ^ ^abWitten, Ian H.; Frank, Eibe; Hall, Mark A.; Pal, Christopher J. (2011). 'Data Mining: Practical machine learning tools and techniques, 3rd Edition'. Morgan Kaufmann, San Francisco (CA). Retrieved 2011-01-19.
2. ^Holmes, Geoffrey; Donkin, Andrew; Witten, Ian H. (1994). 'Weka: A machine learning workbench'(PDF). Proceedings of the Second Australia and New Zealand Conference on Intelligent Information Systems, Brisbane, Australia. Retrieved 2007-06-25.
3. ^Garner, Stephen R.; Cunningham, Sally Jo; Holmes, Geoffrey; Nevill-Manning, Craig G.; Witten, Ian H. (1995). 'Applying a machine learning workbench: Experience with agricultural databases'(PDF). Proceedings of the Machine Learning in Practice Workshop, Machine Learning Conference, Tahoe City (CA), USA. pp. 14–21. Retrieved 2007-06-25.
4. ^'Weka Package Metadata'. SourceForge. 2017. Retrieved 2017-11-11.
5. ^Reutemann, Peter; Pfahringer, Bernhard; Frank, Eibe (2004). 'Proper: A Toolbox for Learning from Relational Data with Propositional and Multi-Instance Learners'. 17th Australian Joint Conference on Artificial Intelligence (AI2004). Springer-Verlag. CiteSeerX10.1.1.459.8443.
6. ^'weka-wiki - Packages'. Retrieved 27 January 2020.
7. ^Witten, Ian H.; Frank, Eibe; Trigg, Len; Hall, Mark A.; Holmes, Geoffrey; Cunningham, Sally Jo (1999). 'Weka: Practical Machine Learning Tools and Techniques with Java Implementations'(PDF). Proceedings of the ICONIP/ANZIIS/ANNES'99 Workshop on Emerging Knowledge Engineering and Connectionist-Based Information Systems. pp. 192–196. Retrieved 2007-06-26.
8. ^Piatetsky-Shapiro, Gregory I. (2005-06-28). 'KDnuggets news on SIGKDD Service Award 2005'. Retrieved 2007-06-25.
9. ^'Overview of SIGKDD Service Award winners'. 2005. Retrieved 2007-06-25.
10. ^'Pentaho Acquires Weka Project'. Pentaho. Retrieved 2018-02-06.
11. ^'Plugin for Machine Intelligence'.
12. ^Thornton, Chris; Hutter, Frank; Hoos, Holger H.; Leyton-Brown, Kevin (2013). Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms. KDD '13 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 847–855.

External links[edit]

Weka 3.6

• Official website at University of Waikato in New Zealand

Weka3.8.4 Download