Download Chapter 2 Describing Data: Graphs and Tables

Data Mining
Overview
Lecture Objectives
After this lecture, you should be able to:
1. Explain key data mining tasks in your own
words.
2. Draw an overview of the Data Mining Process.
3. Discuss one broad business application of data
mining.
4. Explain one way to evaluate effectiveness of a
Data Mining project.
Data Mining Tasks
1. Prediction / Classification
2. Segmentation
3. Association
Course Overview/Techniques Used
Data Preparation
Prediction/Classification
Discriminant Analysis
Logistic Regression
Artificial Neural Networks
Classification Trees (CART, CHAID)
Segmentation
Judgement
Factor Analysis
Cluster Analysis
Association
Market Basket Analysis
Other Correlation Based techniques
Data Mining Process
Source: CRISP-DM (SPSS.com website)
Application in Financial Services
Stage 1
Product
Planning
Customer Stage 2
Acquisition
Customer
Valuation
Stage 4
Collections
and
Recovery
Customer
Management
Stage 3
Measuring Effectiveness:
Percent of potential
responders captured
Lift/Gains Chart
100
Targeting
90
Random mailing
45
0
45
100
Percent of population targeted
Dr. Satish Nargundkar
Discussion
1. Can you think of other applications?
2. What are some limitations of Data Mining?
3. What are future possibilities?