1. Appendix: The WEKA Data Mining Software

2. WEKA: Introduction WEKA, developed by Waikato University, New Zealand.
WEKA (Waikato Environment for Knowledge Analysis)
History: 1st version (version 2.1, 1996); Version 2.3, 1998; Version 3.0, 1999; Version 3.4, 2003; Version 3.6, 2008.
WEKA provides a collection of data mining, machine learning algorithms and preprocessing tools.
It includes algorithms for regression, classification, clustering, association rule mining and attribute selection.
It also has data visualization facilities.
WEKA is an environment for comparing learning algorithms
With WEKA, researchers can implement new data mining algorithms to add in WEKA
WEKA is the best-known open-source data mining software.

3. WEKA: Introduction WEKA was written in Java.
WEKA 3.4 consists of lines of code.
WEKA 3.6 consists of lines of code.
It can work on Windows, Linux and Macintosh.
Users can access its components through Java programming or through a command-line interface.
It consists of three main graphical user interfaces: Explorer, Experimenter and Knowledge Flow.
The easiest way to use WEKA is through Explorer, the main graphical user interface.
Data can be loaded from various sources, including files, URLs and databases. Database access is provided through Java Database Connectivity.

4. WEKA data format WEKA stores data in flat files (ARFF format).
It’s easy to transform EXCEL file to ARFF format.
An ARFF file consists of a list of instances
We can create an ARFF file by using Notepad or Word.
The name of the dataset is
Attribute information is
The data is
Beside ARFF format, WEKA allows CSV, LibSVM, and C4.5’s format.

5. WEKA ARFF format @relation weather
@attribute outlook {sunny, overcast, rainy}
@attribute temperature real
@attribute humidity real
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}
@data
sunny, 85, 85, FALSE, no
sunny, 80, 90, TRUE, no
overcast, 83, 86, FALSE, yes
rainy, 70, 96, FALSE, yes
rainy, 68, 80, FALSE, yes
……………………………

6. Explorer GUI Consists of 6 panels, each for one data mining tasks:
Preprocess
Classify
Cluster
Associate
Select Attributes
Visualize.
Preprocess:
to use WEKA’s data preprocessing tools (called “filters”) to transform the dataset in several ways.
WEKA contains filters for:
Discretization, normalization, resampling, attribute
selection, transforming and combining attributes, …

7. Explorer (cont.) Classify:
Regression techniques (predictors of “continuous classes”)
Linear regression
Logistic regression
Neural network
Support vector machine
Classification algorithms
Decision trees – ID3, C4.5 (called J48)
Naïve Bayes, Bayes network
k-nearest-neighbors
Rule learners: Ripper, Prism
Lazy rule learners
Meta learners (bagging, boosting)

8. Clustering
Clustering algorithms:
K-Means, X-Means, FarthestFirst
Likelihood-based clustering: EM (Expectation-Maximization)
Cobweb (incremental clustering algorithm)
Clusters can be visualized and compared to “true” clusters (if given)
Attribute Selection: This provides access to various methods for measuring the utility of attributes and identifying the most important attributes in a dataset.
Filter method: the attribute set is filtered to produce the most promising subset before learning begins.
A wide range of filtering criteria, including correlation-based feature selection, the chi-square statistic, gain ratio, information, support-machine-based criterion.
A variety of search methods: forward and backward selection, best-first search, genetic search and random search.
PCA (principal component analysis) to reduce the dimensionality of a problem.
Discretizing numeric attributes.

9. Explorer (cont.) Assocation rule mining Visualization
Apriori algorithm
Work only with discrete data
Visualization
Scatter plots, ROC curves,Trees, graphs
WEKA can visualize single attributes (1-d) and pairs of attributes (2-d).
Color-coded class values.
“Zoom-in” function

11. Explorer GUI
(Classify)

12. WEKA Experimenter
This interface is designed to facilitate experimental comparisons of the performance of algorithms based on many different evaluation criteria.
Experiments can involves many algorithms that are run on multiple datasets.
Can also iterate over different parameter settings
Experiments can also be distributed across different computer nodes in a network.
Once an experiment has been set up, it can be saved in either XML or binary form, so that it can be re-visited.

14. Knowledge Flow Interface
The Explorer is designed for batch-based data processing: training data is loaded into memory and then processed.
However WEKA has implemented some incremental algorithms.
Knowledge-flow interface can handle incremental updates. It can load and preprocess individual instances before feeding them into incremental learning algorithms.
Knowledge-flow also provides nodes for visualization and evaluation.

16. Conclusions
Comparison to R, WEKA is weaker in classical statistics but stronger in machine learning (data mining) algorithms.
WEKA has developed a set of extensions covering diverse areas, such as text mining, visualization and bioinformatics.
WEKA 3.6 includes support for importing PMML models (Predictive Modeling Markup Language). PMML is a XML-based standard fro expressing statistical and data mining models.
WEKA 3.6 can read and write data in the format used by the well known LibSVM and SVM-Light support vector machine implementations.
WEKA has 2 limitations:
Most of the algorithms require all the data stored in main memory. So it restricts application to small or medium-sized datasets.
Java implementation is somewhat slower than an equivalent in C/C++

17. References
I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Francisco, 2000.
M. Hall and E. Frank, The WEKA Data Mining Software: An Update, J. SIGKDD Explorations, Vol. 11, No. 1, 2008.
R. R. Bouckaert et al., WEKA Manual for Version 3.6.0, 2008.
E. Frank et al., WEKA – A Machine Learning Workbench for Data Mining, 2003.