1. Introduction to Weka and NetDraw
MIS510
Spring 2009

2. Outline Weka NetDraw Introduction Weka Tools/Functions
How to use Weka?
Weka Data File Format (Input)
Weka for Data Mining
Sample Output from Weka (Output)
Conclusion
NetDraw
How to use NetDraw?
NetDraw Input Data File Format
Draw Networks using NetDraw

3. Weka

4. Introduction to Weka (Data Mining Tool)
Weka was developed at the University of Waikato in New Zealand.
Weka is a open source data mining tool developed in Java. It is used for research, education, and applications. It can be run on Windows, Linux and Mac.

5. What can Weka do?
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset (using GUI) or called from your own Java code (using Weka Java library).
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.

6. Weka Tools/Functions Tools (or functions) in Weka include:
Data preprocessing (e.g., Data Filters),
Classification (e.g., BayesNet, KNN, C4.5 Decision Tree, Neural Networks, SVM),
Regression (e.g., Linear Regression, Isotonic Regression, SVM for Regression),
Clustering (e.g., Simple K-means, Expectation Maximization (EM)),
Association rules (e.g., Apriori Algorithm, Predictive Accuracy, Confirmation Guided),
Feature Selection (e.g., Cfs Subset Evaluation, Information Gain, Chi-squared Statistic), and
Visualization (e.g., View different two-dimensional plots of the data). 6

7. Weka’s Role in the Big Picture
Input
Raw data
Data Ming
by Weka
Pre-processing
Classification
Regression
Clustering
Association Rules
Visualization
Output
Result 7

8. How to use Weka? Weka Data File Format (Input) Weka for Data Mining
Sample Output from Weka (Output)

9. Weka Data File Format (Input)
The most popular data input format of Weka is “arff” (with “arff” being the extension name of your input data file).
FILE FORMAT
@relation RELATION_NAME
@attribute ATTRIBUTE_NAME ATTRIBUTE_TYPR
@data
DATAROW1
DATAROW2
DATAROW3

10. Example of “arff” Input File
@relation heart-disease-simplified
@attribute age numeric
@attribute sex { female, male}
@attribute chest_pain_type { typ_angina, asympt, non_anginal, atyp_angina}
@attribute cholesterol numeric
@attribute exercise_induced_angina { no, yes}
@attribute class { present, not_present}
@data
63,male,typ_angina,233,no,not_present
67,male,asympt,286,yes,present
67,male,asympt,229,yes,present
38,female,non_anginal,?,no,not_present
...
numeric attribute
nominal attribute

11. Weka for Data Mining
There are mainly 2 ways to use Weka to conduct your data mining tasks.
Use Weka Graphical User Interfaces (GUI)
GUI is straightforward and easy to use. But it is not flexible. It can not be called from you own application.
Import Weka Java library to your own java application.
Developers can leverage on Weka Java library to develop software or modify the source code to meet special requirements. It is more flexible and advanced. But it is not as easy to use as GUI.

12. Weka GUI Different analysis tools/functions
The value set of the chosen attribute and the # of input items with each value
Different attributes to choose 12

13. Weka GUI
Classification Algorithms

14. Import Weka Java library to your own Java application
Three sets of classes you may need to use when developing your own application
Classes for Loading Data
Classes for Classifiers
Classes for Evaluation

15. Classes for Loading Data
Related Weka classes
weka.core.Instances
weka.core.Instance
weka.core.Attribute
How to load input data file into instances?
Every DataRow -> Instance, Every Attribute -> Attribute, Whole -> Instances
# Load a file as Instances
FileReader reader;
reader = new FileReader(path);
Instances instances = new Instances(reader);

16. Classes for Loading Data
Instances contains Attribute and Instance
How to get every Instance within the Instances?
How to get an Attribute?
# Get Instance
Instance instance = instances.instance(index);
# Get Instance Count
int count = instances.numInstances();
# Get Attribute Name
Attribute attribute = instances.attribute(index);
# Get Attribute Count
int count = instances.numAttributes();

17. Classes for Loading Data
How to get the Attribute value of each Instance?
Class Index (Very important!)
# Get value
instance.value(index); or
instance.value(attrName);
# Get Class Index
instances.classIndex(); or
instances.classAttribute().index();
# Set Class Index
instances.setClass(attribute); or
instances.setClassIndex(index);

18. Classes for Classifiers
Weka classes for C4.5, Naïve Bayes, and SVM
Classifier: all classes which extend weka.classifiers.Classifier
C4.5: weka.classifier.trees.J48
NaiveBayes: weka.classifiers.bayes.NaiveBayes
SVM: weka.classifiers.functions.SMO
How to build a classifier?
# Build a C4.5 Classifier
Classifier c = new weka.classifier.trees.J48();
c.buildClassifier(trainingInstances);
Build a SVM Classifier
Classifier e = weka.classifiers.functions.SMO();
e.buildClassifier(trainingInstances);

19. Classes for Evaluation
Related Weka classes
weka.classifiers.CostMatrix
weka.classifiers.Evaluation
How to use the evaluation classes?
# Use Classifier To Do Classification
CostMatrix costMatrix = null;
Evaluation eval = new Evaluation(testingInstances, costMatrix);
for (int i = 0; i < testingInstances.numInstances(); i++){
eval.evaluateModelOnceAndRecordPrediction(c,testingInstances.instance(i));
System.out.println(eval.toSummaryString(false));
System.out.println(eval.toClassDetailsString()) ;
System.out.println(eval.toMatrixString()); }

20. Classes for Evaluation
Cross Validation
In cross validation process, we split a single dataset into N equal shares. While taking N-1 shares as a training dataset, the rest will be used as testing dataset.
The most widely used is 10 cross fold validation.

21. Classes for Evaluation
How to obtain the training dataset and the testing dataset?
Random random = new Random(seed);
instances.randomize(random);
instances.stratify(N);
for (int i = 0; i < N; i++) {
Instances train = instances.trainCV(N, i , random);
Instances test = instances.testCV(N, i , random); }

22. Sample Output from Weka

23. Conclusion about Weka
In sum, the overall goal of Weka is to build a state-of-the-art facility for developing machine learning (ML) techniques and allow people to apply them to real-world data mining problems.
Detailed documentation about different functions provided by Weka can be found on Weka website.
WEKA is available at:

24. NetDraw

25. Introduction to NetDraw (Visualization Tool)
NetDraw is an open source program written by Steve Borgatti from Analytic Technologies. It is often used for visualizing both 1-mode and 2-mode social network data.
You can download it from:
(Compared to Weka, it is much easier to use :P)

26. What can NetDraw do? NetDraw can:
handle multiple relations at the same time, and
use node attributes to set colors, shapes, and sizes of nodes.
Pictures can be saved in metafile, jpg, gif and bitmap formats.
Two basic kinds of layouts are implemented: a circle and an MDS based on geodesic distance.
You can also rotate, flip, shift, resize and zoom configurations.

27. How to use NetDraw? NetDraw Input Data File Format
Draw Networks using NetDraw

28. NetDraw Input Data File Format
“vna” Data Format
The VNA data format (with “vna” being the extension name of the input data file) allows users to store not only network data but also attributes of the nodes, along with information about how to display them (color, size, etc.).
*node data
"ID", num
"$10 Gift Card off REGIS SALON (SALON SERVICES) + E" 2
"$10 iTunes Gift Certificate exp 9/2008" 2
"$10 STARBUCKS gift CARD CERTIFICATE" 3
"$10 Target Gift Card" 3
"$10.00 iTunes Music Gift Card - Free Shipping" 2
"$100 Best Buy Gift Card" 15
"$100 Gap Gift Card - FREE Shipping" 9
… … … … … … … …
*Tie data
FROM TO "Strength"
"Home Depot Gift Card $500." "$100 Home Depot Gift Card Accepted Nationwide" 1
"** $250 Best Buy GiftCard Gift Card Gift Certifica" "$25 Best Buy Gift Card for Store or Online!" 1
"$50 Bed Bath & Beyond Gift Card - FREE SHIPPING!" "$200 Cost Plus World Market Gift Card 4 Jewelry Be" 1
"$ Best Buy gift certificate" "$15 Best Buy Gift Card *Free Shipping*" 1
"$25 Best Buy Gift Card for Store or Online!" "$15 Best Buy Gift Card *Free Shipping*" 1
"Bath and Body Works $25 Gift Card" "$200 Cost Plus World Market Gift Card 4 Jewelry Be" 1

29. Draw Networks using NetDraw
Different functions
Display setup of the nodes and relations
The networks: nodes representing the individuals and links representing the relations

30. Analysis Example: Hot Item Analysis based on Giftcard selling information from eBay
Each circle in the graph represents an active item in the database.
The label of the circle is the item title.
The bigger the circle and the label of circle, the hotter the item.
Items are clustered together based on the brand information.
Hot Topics during April 15 – April 22, 2007
Hot Topics during April 22 – April 29, 2007

31. Conclusion
In sum, NetDraw can be used for social network visualization.
There are a lot of parameters to play with in the tool. The results can be saved as EMF, WMF, BMP and JPG files.
NetDraw is available at:
The website also provides detailed documentation.
If you have interest, you may also try some other visualization tools such as JUNG ( and GraphViz (

32. Some Suggestions
Carefully prepare your data according to the input format required by each tool.
Read the documentation of each tool that you decide to use and understand its functionality. Think how it can be applied to your project.
Download and play with the tools. You cannot learn anything unless you try them by yourself!!!

33. Thanks! Good luck for your projects! 