Data Mining Overview.

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Presentation transcript:

Data Mining Overview

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.

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

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

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

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

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