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