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Data Mining >> Content Detail



Lecture Notes



Lecture Notes

 





LEC#TOPICSDATA SOURCES
1Data 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.
2Classification and Bayes Rule, Naïve Bayes (PDF)
3Classification Trees (PDF)"Housing Database (Boston)." Publicly available data at University of California, Irvine School of Information and Computer Science, Machine Learning Repository of Databases.
4Discriminant 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.
5Logistic Regression Case (PDF)

Handlooms (PDF)
6Neural Nets (PDF)
7Discussion of homework - see Problem 1 in assignments section
8Multiple Regression Review (PDF)
9Multiple Linear Regression in Data Mining (PDF)
10Regression Trees, Case: IBM/GM weekly returns

Comparison of Data Mining Techniques (PDF)

Discussion of homework - see Problem 2 in assignments section
11k-Means Clustering, Hierarchical Clustering (PDF)
12Case: Retail Merchandising
13Midterm Exam
14Principal 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.

15Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer
16Association 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.
17Recommendation Systems: Collaborative Filtering
18Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining
 


 



 








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