15.062 | Spring 2003 | Graduate

Data Mining

Lecture Notes

LEC # Topics Data Sources
1

Data Mining Overview (PDF)

Prediction and Classification with k-Nearest Neighbors

Example 1: Riding Mowers (PDF)

Table 11.1 from page 584 of: Johnson, Richard, and Dean Wichern. Applied Multivariate Statistical Analysis. 5th ed. Prentice-Hall, 2002. ISBN: 0-13-092553-5.

2

Classification and Bayes Rule, Naïve Bayes (PDF)

 
3

Classification Trees (PDF)

“Housing Database (Boston).” Publicly available data at University of California, Irvine School of Information and Computer Science, Machine Learning Repository of Databases.

4

Discriminant Analysis Example 2: Fisher’s Iris data (PDF)

“Iris Plant Database.” Publicly available data at University of California, Irvine School of Information and Computer Science, Machine Learning Repository of Databases.

5

Logistic Regression Case (PDF)

Handlooms (PDF)

 
6 Neural Nets (PDF)

 
7

Discussion of homework - see Problem 1 in assignments section

 
8

Multiple Regression Review (PDF)

 
9

Multiple Linear Regression in Data Mining (PDF)

 
10

Regression Trees, Case: IBM/GM weekly returns

Comparison of Data Mining Techniques (PDF)

Discussion of homework - see Problem 2 in assignments section

 
11

k-Means Clustering, Hierarchical Clustering (PDF)

 
12

Case: Retail Merchandising

 
13

Midterm Exam

 
14

Principal Components (PDF)

Example 1, Head Measurements of Adult Sons: Rencher, Alvin. Methods of Multivariate Analysis. 2nd ed. Wiley-Interscience, 2002. Table 3.7, p. 79. ISBN: 0-471-46172-5.

Example 2, Charactersitics of Wine: “Wine Recognition Database.” Publicly available data at University of California, Irvine School of Information and Computer Science, Machine Learning Repository of Databases.

15

Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer

 
16

Association Rules (Market Basket Analysis) (PDF)

Han, Jiawei, and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kauffman Publishers, 2001. Example 6.1 (Figure 6.2). ISBN: 1-55860-489-8.

17

Recommendation Systems: Collaborative Filtering

 
18

Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining

 

Course Info

Instructor
As Taught In
Spring 2003
Level
Learning Resource Types
Problem Sets
Exams
Lecture Notes