1. Knowledge Mining using Machine Learning
Techniques
Mathew J. Palakal
Department of Computer and Information Science
Indiana University Purdue University Indianapolis
Indianapolis Indiana USA
mpalakal@cs.iupui.edu
http://www.cs.iupui.edu/~mpalakal
NIDA
Bangkok, Thailand
December 18-19, 2003
9.00 AM – 4.00 PM
Room 909 Anekprasong Building
SEMINAR SUMMARY
Data Mining has become an emerging discipline in recent years due to
the wide availability of huge amounts of data and the imminent need for
turning such data into useful information and knowledge. The information
and knowledge gained can be used for applications from business
management, production control, and market analysis, to the design and
science exploration. Data (Knowledge) mining is the task of discovering
interesting patterns from large amounts of data where the data can be stored
in databases, data warehouses, or other information repositories. Data
mining involves one or more computer learning techniques to automatically
analyze and extract knowledge from data.
In this seminar basic data mining concepts, strategies, and techniques
will be introduced. Participants will learn about the types of problems that
can be effectively applied for data mining algorithms. Discussions on specific
algorithms and techniques would include decision tree algorithms, the apriori
algorithm for producing association rules, the artificial neural network
approaches for supervised learning, and the K-Means algorithm for
unsupervised clustering. Tools will be provided to help determine which
data mining techniques should be used to solve specific type of application
problems.

2. SEMINAR OUTLINE
Thursday, Dec 18: Morning
1. Data Mining Overview
• What can Computers learn?
• Supervised learning
• Unsupervised learning
• Mining Structured Data
• Mining Unstructured Data
• Data Mining Process Model
• Data Mining Applications
• iDA – A tool for Data Mining
Thursday, Dec 18: Afternoon
2. Supervised Data Mining Algorithms
• Classification, Estimation, Prediction
• Decision Trees
• Association Rules
• Neural Network algorithms
• Case Study
Friday, Dec 19: Morning
3. Unsupervised Data Mining Algorithms
• Clustering Algorithms
• Neural Network algorithms
• Case Study
Friday, Dec 19: Afternoon
4. Unsupervised Data Mining Algorithms
• Clustering Algorithms
• Neural Network algorithms
• Case Study