1. BUSINESS INTELLIGENCE
AND DATA WAREHOUSING.
Presented by:
Vaishnavi Chigarapalle.

2. Agenda:
• ID3 Algorithm.
• WEKA.
• Web Mining Applications for Business.
• References.

3. ID3 Algorithm.

4. Overview:
• What is ID3 ?
• Decision Trees.
• Simple example of Decision Trees.
• ID3 Algorithm.
• Problem.
• Solution to the discussed problem.
• Conclusion.

5. What is ID3 ?
• ID3 Stands for Iterative Dichotomiser 3.
• This is a mathematical algorithm for building Decision Trees from a
dataset.
• Invented by J . Ross Quinlan in 1979.
• Uses Information Theory invented by Shannon in 1948.
• The algorithm attempts to create smallest possible decision tree from
top down, with no backtracking.
• ID3 is the precursor to the C4.5 algorithm.
• This is typically used in machine learning and Natural Language
Processing Domains.

6. Decision trees
• The tree consists of decision nodes and leaf nodes.
• A decision node has two or more branches, each representing values for the
attribute set.
• A leaf node attribute produces a homogeneous result, which does not
require additional classification testing.
• Decision trees are produced by algorithms that identify various ways of
splitting a data set into branch-like segments.
• These segments form an inverted decision tree that originates with a root
node at the top of the tree.

7. Simple Example of a Decision Tree.

8. ID3 Algorithm
• First step involves creating a root node for the tree.
• If all the examples turn out to be containing positive values then return the
single-node r=tree root, with label „+‟.
• If all the examples turn out to be containing negative values then return the
single-node root, with label „-„.
• If the number of predicting attributes is empty, then return the single node
tree root, with label being the most common value of the target attribute.
• Else
A = Attribute that best classifies examples.
Decision tree attribute for root that equals to A.
For each possible value, vi, of A,
 Add a new tree branch below root, corresponding to the test A = vi.

9. ID3 Algorithm
 Let examples (vi), be the subset of examples that have the value vi
for A.
 If examples (vi) is empty
 Then below this new branch add a leaf node with label equal to most
common target value in the examples.
– Else below this new branch add the subtree ID3 (Examples
(vi), Target_Attribute, Attributes-{A}).
• End
• Return Root.

10. Conclusion
• ID3 attempts to make the shortest decision tree out of a set of learning
data, shortest is not always the best classification.
• Requires learning data to have completely consistent patterns with no
uncertainty.

11. WEKA
(University of Waikato)

12. Overview
• What is WEKA ?
• WEKA GUI Chooser.
• Data Mining with WEKA.
• Problem.
• Solution for the discussed problem.
• Conclusion

13. What is WEKA ?
• WEKA is an acronym for Waikato Analysis for Knowledge Analysis.
• This is a popular suite of machine learning software written in Java.
• This is developed at University of Waikato, New Zealand.
• WEKA is portable, since it is fully implemented in the Java programming
language and thus runs on almost any modern computing platform.
• WEKA is free software available under the GNU General Public License.
• WEKA‟s applications:
 Explorer.
 Knowledge Flow.
 Experimenter.
 Simple CLI.

14. WEKA GUI Chooser.

15. Data Mining With WEKA
Input
•Raw data
Data Mining by WEKA
•Pre-processing
•Classification
•Regression
•Clustering
•Association Rules
•Visualization
Output
•Result

16. Explorer
• Explorer is WEKA‟s main user interface.
• The Explorer interface features several panels providing access to the main
component of the work bench :
 Preprocess.
 Classify
 Associate
 Cluster
 Select Attributes
 Visualize.
• Preprocess Panel: This can be used to transform the data and make it
possible to delete the instances and attributes according to specific criteria.
• Classify Panel: Enables the users to apply classification and regression
algorithms to resulting dataset, to estimate accuracy of the resulting
predictive model.

17. • Associate Panel: This provides access to association rule learners that
attempt to identify all important interrelationships between attributes in the
data.
• Cluster Panel: This gives access to the clustering techniques in WEKA.
• Select Panel: This panel provides algorithms for identifying the most
predictive attributes in a dataset.
• Visualize Panel: This panel shows a scatter plot matrix, where individual
scatter plots can be selected and enlarged, and analyzed further using
various selection operators.

19. Experimenter
• This allows the systematic comparison of the predictive performance of
WEKA‟s machine learning algorithms on a collection of datasets.
• Experimenter also allows us to set large-scale experiments, start them
running, leave them, and they analyze the performance statistics that have
been collected.
• They automate the experimental process.
• The statistics can be stored in ARFF format.
• It allows users to distribute the computing load across multiple machines
using Java RMI.

20. The Experimenter

21. Knowledge Flow
• The Knowledge Flow provides an alternative to the Explorer as a graphical
front end to WEKA‟s core algorithms.
• The Knowledge Flow presents a data-flow inspired interface to WEKA.
• The user can select WEKA components from a tool bar, place them on a
layout canvas and connect them together in order to form a knowledge for
Flow processing and analyzing data.
• Unlike the Explorer the Knowledge Flow can handle data either
incrementally or in batches.

22. Knowledge-Flow

23. Simple CLI
• Simple CLI provides a command line mode to access WEKA.

24. Conclusion
• 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.

25. WEB MINING

26. Overview
• What is Web mining ?
• Challenges related to web mining.
• Web mining applications.
• Problems with Web search.
• Improvised search – adding structure to the web.
• Conclusion.

27. What is Web Mining ?
• Web mining is the use of data mining techniques to automatically discover
and extract information from web documents / services.
• Discovering useful information from the World-wide Web and its usage
patterns.
• Web mining can be divided into three different type:
 Web usage mining.
 Web Content mining.
 Web structure mining.

28. Challenges related to Web Mining
• The web is a huge collection of documents except for the following:
 Hyperlink information
 Access and usage information.
• The web is very dynamic, new pages are constantly being generated.
• Challenge: The main challenge is to develop new web mining algorithms
and adapt traditional data mining algorithms to exploit hyperlinks and
access patterns.

29. Web Mining Applications
• E-Commerce (Infrastructure)
 Generate User profiles.
 Internet Advertising.
 Fraud.
 Similar Image Retrieval.
• Information retrieval (search) on web
 Automatic generation of topic hierarchies.
 Web Knowledge bases.
 Extraction of schema for XML documents.
• Network Management
 Performance Management.
 Fault Management.

30. User Profiling.
• Important for improving customization:
 Provides users with pages, advertisements of interest.
 Example profiles: on-line trader, on-line shopper.
• Generate user profiles based on their access patterns
 Cluster users based on frequently accessed URLs
 Use classifier to generate a profile for each cluster.

31. Internet Advertising.
• Scheme 1:
 Manually associate a set of ads with each user profile.
 For each user, display an ad from the set based on profile.
• Scheme 2:
 Automate association between ads and users.
 Use ad click information to cluster users.
 For each cluster, find ads that occur most frequently in the cluster and these
become the ads for the set of users in the cluster.

32. Fraud
• With the growing popularity of E-commerce, systems to detect and prevent
fraud on the web become important.
• Maintain a signature for each user based on buying patterns on the web.
• If buying pattern changes significantly, then signal fraud.
• HNC software uses domain knowledge and neural networks for credit card
fraud detection.

33. Image Retrieval System
• Given:
 A set of images
• Find:
 All images similar to a given image.
 All pairs of similar images.
• Few applications of the image retrieval system are :
 Medical diagnosis.
 Weather Prediction
 Web search engine for images.
 E-commerce.

34. Problems with Web Search
• Today‟s search engine are plagued by many problems and few of them are
as mentioned below:
 The “abundance” problem.
 “Limited coverage” of the web.
(largest crawlers cover less than 18% of all the web pages.
 “Limited Query” interface based on keyword-oriented search.
 “Limited customization” to individual users.
 Web is “highly dynamic”.

35. Improvised searching – Adding
structure to the web

36. Conclusion
• Web mining systems needs to be implemented to:
 Understand visitor‟s profiles.
 Identify company‟s strength and weaknesses.
 Measure the effectiveness of online marketing efforts.
• Web mining support on-going continuous improvements for E-businesses.

37. References
• http://www.slideshare.net/dataminingtools/WEKA-the-experimenter
• http://www.cs.waikato.ac.nz/ml/WEKA/arff.html
• http://en.wikipedia.org/wiki/WEKA_(machine_learning)
• http://www.cs.umd.edu/Grad/scholarlypapers/papers/Bahety.pdf
• http://software.ucv.ro/~eganea/AIR/KnowledgeFlowTutorial-3-5-8.pdf