1. Data Mining
Overview

2. Lecture Objectives After this lecture, you should be able to:
Explain key data mining tasks in your own words.
Discuss one broad business application of data mining.
Explain one way to evaluate effectiveness of a Data Mining project.

3. Data Mining Tasks Description/Visualization Segmentation
Charts/Graphs/Tabulations
Segmentation
Cluster Analysis
Prediction / Classification
Regression Techniques – Linear, Logistic
Association
Market Basket Analysis
Optimization
Linear Programming

4. Course Overview/Techniques Used
Data Preparation
Prediction/Classification
Linear Classification (Discriminant Analysis)
Classification Trees (CART, CHAID)
Logistic Regression
Artificial Neural Networks
Segmentation
Judgment
Cluster Analysis
Factor Analysis
Association
Matching techniques
Market Basket Analysis

5. Application in Financial Services
Product
Planning
Customer
Acquisition
Collections
and
Recovery
Customer Manage-ment
Valuation
Stage 1
Stage 2
Stage 4
Stage 3

6. Measuring Effectiveness: Lift/Gains Chart
Targeting
100 90
Percent of potential
responders captured
Random mailing 45 45
100
Percent of population targeted
Dr. Satish Nargundkar

7. Discussion Can you think of other applications?
What are some limitations of Data Mining?
What are future possibilities?