MIS510

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Introduction to Weka and NetDraw

• MIS510
• Spring 2009

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Outline

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

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Weka
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Introduction to Weka (Data Mining Tool)

• Weka was developed at the University of Waikato
  in New Zealand. http//www.cs.waikato.ac.nz/ml/wek
  a/
  
• 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.

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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.

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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).

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Wekas Role in the Big Picture
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How to use Weka?

• Weka Data File Format (Input)
• Weka for Data Mining
• Sample Output from Weka (Output)

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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
• _at_relation RELATION_NAME
• _at_attribute ATTRIBUTE_NAME ATTRIBUTE_TYPR
• _at_attribute ATTRIBUTE_NAME ATTRIBUTE_TYPR
• _at_attribute ATTRIBUTE_NAME ATTRIBUTE_TYPR
• _at_attribute ATTRIBUTE_NAME ATTRIBUTE_TYPR
• _at_data
• DATAROW1
• DATAROW2
• DATAROW3

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Example of arff Input File

• _at_relation heart-disease-simplified
• _at_attribute age numeric
• _at_attribute sex female, male
• _at_attribute chest_pain_type typ_angina, asympt,
  non_anginal, atyp_angina
  
• _at_attribute cholesterol numeric
• _at_attribute exercise_induced_angina no, yes
• _at_attribute class present, not_present
• _at_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
• ...

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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.

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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
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Weka GUI
Classification Algorithms
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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

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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 re
ader new FileReader(path) Instances instances
new Instances(reader)
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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.inst
ance(index) Get Instance Count int count in
stances.numInstances()
Get Attribute Name Attribute attribute insta
nces.attribute(index) Get Attribute Count int
count instances.numAttributes()
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Classes for Loading Data

• How to get the Attribute value of each
  Instance?
  
• Class Index (Very important!)

Get value instance.value(index) or ins
tance.value(attrName)
Get Class Index instances.classIndex()
or instances.classAttribute().index()
Set Class Index instances.setClass(attribute)
or instances.setClassIndex(index)
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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 wek
a.classifier.trees.J48() c.buildClassifier(train
ingInstances) Build a SVM Classifier Classifie
r e weka.classifiers.functions.SMO()
e.buildClassifier(trainingInstances)
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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 n
ew Evaluation(testingInstances, costMatrix)
for (int i 0 i s() i) eval.evaluateModelOnceAndRecordPredict
ion(c,testingInstances.instance(i))
System.out.println(eval.toSummaryString(false))
System.out.println(eval.toClassDetailsString())
System.out.println(eval.toMatrixString())
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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.

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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 rain instances.trainCV(N, i , random)
Instances test instances.testCV(N, i ,
random)
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Sample Output from Weka
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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
• http//www.cs.waikato.ac.nz/ml/weka

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NetDraw
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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
• http//www.analytictech.com/downloadnd.htm
• (Compared to Weka, it is much easier to use P)

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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.

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How to use NetDraw?

• NetDraw Input Data File Format
• Draw Networks using NetDraw

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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 SA
LON (SALON SERVICES) E" 2 "10 iTunes Gift Cert
ificate exp 9/2008" 2 "10 STARBUCKS gift CARD CE
RTIFICATE" 3 "10 Target Gift Card" 3 "10.00 iT
unes Music Gift Card - Free Shipping" 2
"100 Best Buy Gift Card" 15 "100 Gap Gift Card
- FREE Shipping" 9 Tie data FR
OM TO "Strength" "Home Depot Gift Card 500." "1
00 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 - FRE
E SHIPPING!" "200 Cost Plus World Market Gift
Card 4 Jewelry Be" 1 "500.00 Best Buy gift certi
ficate" "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
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Draw Networks using NetDraw
Different functions
Display setup of the nodes and relations
The networks nodes representing the individuals
and links representing the relations
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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

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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
• http//www.analytictech.com/downloadnd.htm
• The website also provides detailed documentation.
  
  
• If you have interest, you may also try some other
  visualization tools such as JUNG
  (http//jung.sourceforge.net/) and GraphViz
  (http//www.graphviz.org/).

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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!!!

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Thanks!Good luck for your projects! ?