Category Intelligent Software>Data Mining Systems/Tools

Abstract Weka (Waikato Environment for Knowledge Analysis) is 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, clustering, association rules, and visualization. It is also well-suited for developing ‘new machine learning’ schemes.

The Weka workbench contains a collection of ‘visualization tools’ and algorithms for data analysis and predictive modeling, together with Graphical User Interfaces (GUIs) for easy access to this functionality.

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 pre-processing 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, 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.

The main strengths of Weka are:

1) Freely available under the GNU General Public License;

2) Very portable because it is fully implemented in the Java programming language and thus runs on almost any modern computing platform;

3) Contains a comprehensive collection of data pre-processing and modeling techniques; and

4) It is easy to use by a novice due to the graphical user interfaces it contains.

Weka supports several standard data mining tasks/tools, as stated above.

All of Weka's techniques are predicated on the assumption that the data is available as a ‘single 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 ‘SQL databases’ using Java Database Connectivity (JDBC) and can process the result returned by a database query.

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 via Proper (Proper - A Toolbox for Learning from Relational Data with Propositional and Multi-Instance Learners).

Another important area that is currently Not covered by the algorithms included in the Weka distribution is sequence modeling.

Weka's 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 Weka's machine learning algorithms on a collection of datasets.

The Explorer interface has several panels that give 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 pre-processing this data using a so-called filtering algorithm.

These filters can be used to transform the data (e.g., turning numeric attributes into discrete ones) and make it possible to delete instances and attributes according to specific criteria.

The Classify panel enables the user to apply 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, ROC curves, etc., or the model itself (if the model is amenable to visualization, e.g., a decision tree).

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. g., the simple ‘K-means algorithm’.

There is also an implementation of the ‘expectation maximization’ algorithm for learning a mixture of normal distributions.

The next panel, Select attributes provides algorithms for identifying the most ‘predictive attributes’ in a dataset.

The last panel, Visualize, shows a scatter-plot matrix, where individual scatter plots can be selected and enlarged, and analyzed further using various selection operators.

System Requirements

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Manufacturer Web Site Weka

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G6G Abstract Number 20534

G6G Manufacturer Number 104150