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Data Mining >> Content Detail



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LEC #TOPICS
Introduction
1Data Mining Overview, Prediction and Classification with k-Nearest Neighbors
Classification
2Classification and Bayes Rule, Naïve Bayes
3Classification Trees (Homework 1 given out)
4Discriminant Analysis
5Logistic Regression Case: Handlooms
6Neural Nets
7Cases: Direct Marketing/German Credit (Homework 1 due)(Homework 2 given out)
Prediction
8Assessing Prediction Performance
9Subset Selection in Regression
10Regression Trees, Case: IBM/GM weekly returns (Homework 2 due)
Clustering
11k-Means Clustering, Hierarchical Clustering
12Case: Retail Merchandising
13Midterm Exam
Dimension Reduction
14Principal Components
15Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer
Data Base Methods
16Association Rules (Market Basket Analysis)
17Recommendation Systems: Collaborative Filtering
Wrap Up
18Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining
19Project Presentations
 


 



 








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