WEKA is an open source data mining and machine learning software written in Java. It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. WEKA was developed at the University of Waikato and contains algorithms for classification like decision trees, clustering like k-means, and preprocessing tools. The document provides examples of using WEKA's clustering and decision tree classification algorithms on sample investment data to segment investors and predict investment choices.

1. IT & BUSINESS INTELLIGENCE
DATA MINING
ON
WEKA
SATYAM KHATRI
(10BM60081)
MBA, VGSOM
IIT KHARAGPUR

2. WEKA
WEKA is a collection of open source many data mining and machine learning algorithms. It was created
by researchers at the University of Waikato in New Zealand, it is a Java based, open source tool. WEKA
is used for pre-processing on data, Classification, clustering and association rule extraction
It’s main features are as follows
49 data preprocessing tools
76 classification/regression algorithms
8 clustering algorithms
15 attribute/subset evaluators + 10 search algorithms for feature selection.
3 algorithms for finding association rules
3 graphical user interfaces
“The Explorer” (exploratory data analysis)
“The Experimenter” (experimental environment)
“The Knowledge Flow” (new process model inspired interface)
WEKA FUNCTIONS AND TOOLS
Preprocessing Filters
Attribute selection
Classification/Regression
Clustering
Association discovery
Visualization
DOWNLOAD INSTRUCTIONS
Download Weka (the stable version) from http://www.cs.waikato.ac.nz/ml/weka/
Choose a self-extracting executable (including Java VM)
If you are interested in modifying/extending weka there is a developer version that includes the
source code
WEKA DATA FORMATS
Data can be imported from a file in various format such as ARFF, CSV, C4.5. Data can also be read from
a URL or from an SQL database (using JDBC)

3. CLUSTERING
A cluster, by definition, is a group of similar objects. There could be clusters of people, brands or other
objects. If clusters are formed of customers similar to one another, then cluster analysis can help
marketers identify segments (clusters).If clusters of brands are formed, this can be used to gain insights
into brands that are perceived as similar to each other on a set of attributes. Cluster analysis is hence
used for customer segmentation. Cluster analysis is best performed when the variables are interval or
ratio-scaled
There are two major classes of cluster analysis techniques
hierarchical
non-hierarchical
HIERARCHICAL CLUSTERING
Some measure of distance is used to identify distances between all pairs of objects to be clustered. One
of the popular distance measures used is Euclidean Distance. Another is the Squared Euclidean
Distance. We begin with all objects in separate clusters. Say, we have ten objects in separate clusters.
Two closest objects are joined to form a cluster. The remaining 8 objects would remain separate. This is
stage 1 of hierarchical clustering.
NON HIERARCHICAL CLUSTERING
They are also known as k-means clustering methods, we need to specify the number of clusters we want
the objects to be clustered into. This can be done if we have a hypothesis that the objects will group into a
certain number of clusters. Alternatively, we can first do a hierarchical clustering on the data, find the
approximate number of clusters, and then perform a k-means clustering
IMPLEMENTATION METHODS
k - Means
EM
Cobweb
X-means
Farthest First

4. CLUSTERING ON WEKA
PROBLEM CASE
An Asset Management company (AMC) wants to launch a new Mutual Fund Scheme, AMC wants to
segment the target market, so that it can raise funds easily by different marketing strategies for different
segments of target market.
AMC segments the target market on the basis of following parameters
1. Investor’s Age
2. Marital status
3. Investor’s Monthly income
4. Region of Residence
5. Investment in Derivatives
6. Investment in Equities
7. Investment in Fixed deposits
8. Investment in Gold
9. Existing number of Mutual fund schemes
10. Existing loans
Data is collected from the public base on the above parameters and clustering function is performed on it
WEKA Explorer interface

5. Processing on parameter Investment in Gold
Processing on parameter Existing Number of Mutual fund schemes

6. Processing on parameter Existing Loans
Processing on parameter “Age of Investor “

7. Processing on parameter Investment in Fixed deposits
Processing on parameter Investor’s marital status

8. Processing on parameter “Investor’s region of residence”
Processing on parameter “Investor’s monthly income”

9. Processing on parameter Investment in derivatives
Visualization of the entire dataset

10. To perform clustering, select the "Cluster" tab in the Explorer and click on the "Choose" button. This
results in a drop down list of available clustering algorithms. In this case we select "Simple K Means".
Next, click on the text box to the right of the "Choose" button to get the pop-up window shown k-means
clustering is done by dividing the data into 4 cluster group.
The WEKA Simple K Means algorithm uses Euclidean distance measure to compute distances between
instances and clusters. In the pop-up window we enter 6 as the number of clusters (instead of the default
values of 2) and we leave the value of "seed" as is. The seed value is used in generating a random
number which is, in turn, used for making the initial assignment of instances to clusters. Note that, in
general, K-means is quite sensitive to how clusters are initially assigned. Thus, it is often necessary to try
different values and evaluate the results
Once the options have been specified, we can run the clustering algorithm. Here we make sure that in the
"Cluster Mode" panel, the "Use training set" option is selected, and we click "Start". We can right click the
result set in the "Result list" panel and view the results of clustering in a separate window.

11. CLUSTERING RESULTS

12. Clusters can be visualize as shown below
CLUSTER 1
It consist of people with average age of 44 yrs, mostly male, that stay in town, have average monthly
income of 30000, mostly single and invest in equities, fixed deposits, gold, do not invest in derivatives and
have existing loans.
CLUSTER 2
It consist of people with average age of 49 yrs, mostly male, that stay in town, have average monthly
income of 39000, mostly married and invest in equities, fixed deposits, gold, do not invest in derivatives
and have existing loans.
CLUSTER 3
It consist of people with average age of 39 yrs, mostly male, that stay in cities, have average monthly
income of 24000, mostly married and invest in gold, derivatives, do not invest in equities and fixed
deposits, and have existing loans.
CLUSTER 4
It consist of people with average age of 40 yrs, mostly female, that stay in cities, have average monthly
income of 25000, mostly married and invest in equities, fixed deposits, do not invest in derivatives, gold
and have existing loans.

13. CLASSIFICATION VIA DECISION TREES IN WEKA
PROBLEM CASE
A market research firm wants to model the investment decisions by people in various types of securities
on the basis of following parameters Investor’s Age, Marital status, Investor’s Monthly income, Region of
Residence, Investment in Derivatives, ,Investment in Equities, Investment in Fixed deposits, Investment in
Gold, Investment in Mutual funds, Existing loans. Based on this model, an investment decision by an
entity in a particular type of security can be predicted if other parameters about that entity are mentioned
Data is collected from the public on the above parameters and classification is done
Next, we select the "Classify" tab and click the "Choose" button to select the J48 classifier, Note that J48
(implementation of C4.5 algorithm does not require discretization of numeric attributes, in contrast to the
ID3 algorithm from which C4.5 has evolved. Now, we can specify the various parameters. These can be
specified by clicking in the text box to the right of the "Choose" button, In this example we accept the
default values. The default version does perform some pruning (using the sub tree raising approach), but
does not perform error pruning.

15. Under the "Test options" in the main panel we select 10-fold cross-validation as our evaluation approach.
Since we do not have separate evaluation data set, this is necessary to get a reasonable idea of accuracy
of the generated model. We now click "Start" to generate the model. The ASCII version of the tree as well
as evaluation statistics will appear in the eight panel when the model construction is completed We can
view this information in a separate window by right clicking the last result set (inside the "Result list" panel
on the left) and selecting "View in separate window" from the pop-up menu.

16. We can also use our model to classify the new instances. In the main panel, under "Test options" click the
"Supplied test set" radio button, and then click the "Set..." button. This will pop up a window which allows
you to open the file containing test instances.

17. This, once again generates the models from our training data, but this time it applies the model to the new
unclassified instances in order to predict the value of an attribute. Note that the summary of the results in
the right panel does not show any statistics.
WEKA also let's us view a graphical rendition of the classification tree. This can be done by right clicking
the last result set (as before) and selecting "Visualize tree" from the pop-up menu.
Note that by resizing the window and selecting various menu items from inside the tree view (using the
right mouse button), we can adjust the tree view to make it more readable.