More about Weka

Getting Started

1 Weka Installation

Weka requires Java. We may already have Java installed and if not, there are versions of Weka listed on the download page that include Java. The latest version of Weka can be downloaded from http://www.cs.waikato.ac.nz/ml/weka/

2 User Interfaces

The GUI chooser in Weka consist of four options:
• Explorer: It is an environment for exploring data with WEKA. 
• Experimenter: It is used for performing experiments and conducting statistical tests between learning schemes.
• Knowledge Flow: It supports same functions as the Explorer but with a drag and drop interface. 
• Simple CLI: This is a simple command-line interface that allows direct execution of WEKA commands for operating systems that do not provide their own command line interface.

3 Package Installation

Choose Tools -> Package Manager from Weka GUI Chooser. The package manager’s window is split horizontally into two parts: at thetop is a list of packages and at the bottom is a mini browser that can be usedto display information on the currently selected package.

4 Data Sets

Each entry in a dataset is an instance of the java class: weka.core.Instance. 
4.1 ARFF
An ARFF (Attribute-Relation File Format) file is an ASCII text file thatdescribes a list of instances sharing a set of attributes. It consist of:
• header section is prefixed by @RELATION
• each attribute is indicated by @ATTRIBUTE
• data section is prefixed by  @DATA 
• data is comma separated, with the class as the last attribute
 4.2 XRFF
The XRFF (Xml attribute Relation File Format) is a representing the data ina format that can store comments, attribute and instance weights.The following file extensions are recognized as XRFF files:
• .xrff: The default extension of XRFF files
• .xrff.gz: The extension for gzip compressed XRFF files 
4.3 Converters
Weka contains converters for the following data sources:
• ARFF files (ArffLoader, ArffSaver)
• C4.5 files (C45Loader, C45Saver)
• CSV files (CSVLoader, CSVSaver)
• Files containing serialized instances (SerializedInstancesLoader, SerializedInstancesSaver)
• JDBC databases (DatabaseLoader, DatabaseSaver)
• libsvm files (LibSVMLoader, LibSVMSaver)
• XRFF files (XRFFLoader, XRFFSaver)
• Text directories for text mining (TextDirectoryLoader)
4.4 Databases
Weka comes with example files for the following databases:
• DatabaseUtils.props.hsql - HSQLDB (>= 3.4.1)
• DatabaseUtils.props.msaccess - MS Access (> 3.4.14, > 3.5.8, > 3.6.0)
• DatabaseUtils.props.mssqlserver- MS SQL Server 2000 (>= 3.4.9, >= 3.5.4)
• DatabaseUtils.props.mssqlserver2005 - MS SQL Server 2005 (>= 3.4.11, >= 3.5.6)
• DatabaseUtils.props.mysql - MySQL (>= 3.4.9, >= 3.5.4)
• DatabaseUtils.props.odbc - ODBC access via Sun’s ODBC/JDBC drivers
• DatabaseUtils.props.oracle - Oracle 10g (>= 3.4.9, >= 3.5.4)
• DatabaseUtils.props.postgresql - PostgreSQL 7.4 (>= 3.4.9, >= 3.5.4)
• DatabaseUtils.props.sqlite3 - sqlite 3.x (> 3.4.12, > 3.5.7)

5 Using the API

Weka provides an API that can be directly invoked from Java code. It can be used to embed machine learning algorithms in Java programs. The following are the steps required to embed a classifier:
Step 1: Express the problem with features
Step 2: Train a Classifier
Step 3: Test the classifier
Step 4: use the classifier

6 Weka Integration to Pentaho Data Integration

Weka can be easily integrated with the ETL tool Spoon using the Weka Scoring Plugin. The following steps are to be followed for the plugin installation:
1. The Weka scoring plugin can be downloaded from: http://wiki.pentaho.com/display/EAI/List+of+Available+Pentaho+Data+Integration+Plug-In 
2. Unpack the plugin archive and copy all files in the WekaScoringDeploy directory to a sub-directory in the plugins/steps directory of the Kettle installation. 
3. Copy the "weka.jar" file from the Weka installation folderto the same sub-directory in plugins/steps as before.

7 Pros and Cons of Weka

7.1 Advantages
• Open source
• Extensible
• Portable
• Relatively easier to use 
• Large collection of Data Mining algorithms
7.2 Disadvantages
• Sequence modelling is not covered by the algorithms included in the Weka distribution 
• Not capable of multi-relational data mining 
• Memory bound

8 Projects based on Weka

There are many projects that extend or wrap WEKA. Some of these include:
• Systems for natural language processing: GATE is an NLP tool that uses Weka for natural language processing.
• Knowledge discovery in biology: BioWEKA is anextension to WEKA for tasks in biology
• Distributed and parallel data mining: There are a number of applications that use Weka for distributed data mining. Some of them include Weka- Parallel, Grid Weka, FAEHIM and Weka4WS. 
• Open-source data mining systems: Many data mining systems provide plugins to access Weka’s algorithms. The R statistical computing environment provides an interface to Weka using RWeka package. 
• Scientific workflow environment: The Kepler open- source scientific workflow platform is developed based on Weka.

9 Alternatives to Weka

The following are the main alternatives to Weka:

• R is a powerful statistical programming language. It is derived from the S language which was developed by Bell Labs.
• ELKI is a similar project to Weka with a focus on cluster analysis 
• KNIME is a machine learning and data mining software implemented in Java.
• 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 written in Python and C++.
• RapidMiner is a commercial machine learning framework implemented in Java which integrates Weka.

One of the most challenging aspects of open source software is to decide what to include in the software. So the contributions to the software are controlled. This limits the community involvement. It can be managed by using packages. The package management system of Weka is the best example for this. The mailing list of open source software are easier to maintain if the users are researchers. Weka is developed and maintained by a team of researchers at Waikato University. One of the main advantage of using Weka is that it has been incorporated into many open source projects. Hence for a beginner in data mining, among the available open source projects Weka forms the best choice.

Head Up, Stay Strong, Fake a Smile, Move on.....

Weka

With the advent of Search Engines and Social Media Sites, there is an explosion of data. Today’s age can be regarded as “We are drowning in data, but starving for knowledge”. Companies are spending millions to build data warehouses for storing the data. But most of the companies fail in getting the expected ROI from this data. Here comes the importance of data mining. Data mining is the process of gaining knowledge by analyzing the patterns and trends in the data. Different data mining tools such as R, Rapid Miner and Weka are used for this purpose. Weka stands for Waikato Environment for Knowledge Analysis. It is a statistical and data analysis tool written in Java. Weka was developed by a team of researchers at Waikato University in New Zealand. Weka is a collection of visualization tools and algorithms for data analysis. It supports most of the standard data mining tasks such as data preprocessing, clustering, classification, regression, visualization and feature selection. Weka is an open source data mining software and is available under GNU General Public License agreement. It was originally written in C and later was rewritten to Java. Hence it is compatible with all computing platforms. It also provides a GUI for ease of use. Weka works on the assumption that the data is available as flat file where the different attributes in the data set is fixed. The most stable version of Weka is 3.6.13 which was released on September 11, 2015. 

Features of Weka

The following are the important features of Weka:

2.1.1 Open Source software

          Weka is freely available under the GNU GPL. The source code of Weka is written in Java.

2.1.2 Designed for data analysis

          It consists of a vast collection of algorithms for data mining and machine learning. Weka is kept up-to-date with new algorithms being added.

2.1.3 Ease of use

             It is easily useable by people who are not data mining specialists 

2.1.4 Platform independence

           Weka is platform independent.

Functionalities provided by Weka

The following are the basic functionalities provided by Weka:

• Data Preprocessing: Weka supports various data formats including the database connectivity using JDBC.
• Classification:Weka includes more than 100 classification algorithms. Classifiers are divided into Bayesian methods(Naïve Bayes, Bayesian nets etc), lazy methods(nearest neighbor and variants), rule based methods(decision tables, OneR, RIPPER etc), tree learners(C4.5, Naïve Bayes, M5), function based learners(Linear Regression, SVM, Gaussian Process) and other miscellaneous methods.
• Clustering: The various clustering algorithms implemented in Weka includes K-means, EM and other hierarchical methods.
• Attribute selection: The classifier performance depends on the attributes selected. Various search methods and selection criteria are available for attribute selection.
• Data visualization: Various visualization options include Tree viewer, Dendrogram viewer and Bayes Network Viewer.

More updates about Weka in the next post.

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