CS 391 - Selected Topics: Data Mining
Readings


Unless otherwise noted, all readings are from the course text.  Each reading assignment should be completed before the class on the date indicated.  These readings are subject to change; check here for updates.  If a reading assigned in class does not match the reading assignment here, the reading assigned in class supercedes.
Class Month Day Topic Readings (parenthesized
reading are optional)
1 January 17 Introduction, course overview  
2   22 Data mining: examples, basic definitions, ethics Frank & Witten, Chapter 1
3   24 Data mining input: ARFF format, potential problems Ch. 2
4   29 Data mining output: Decision tables and trees, classification and association rules, rule exceptions 3.1-3.5
5   31 Data mining output: Relational rules, regression trees, model trees, instance-based representation, clusters 3.6-3.9
6 February 5 Algorithms: 1R 4.1
7   7 Algorithms: Nave Bayes 4.2
8   12 Algorithms: top-down decision tree induction 4.3
9   14 Algorithms: PRISM 4.4
10   19 Algorithms: association rules through dynamic programming 4.5
11   21 Algorithms: linear/logistic regression, perceptrons 4.6
12   26 Algorithms: nearest neighbor, kD-trees, ball-trees 4.7
13   28 Algorithms: k-means clustering 4.8
14 March 11 Evaluation (Chapter 5)  
15   13    
16   18    
17   20    
18   25 (class cancelled - away at conference)  
19   27 (class cancelled - away at conference)  
20 April 1    
21   3    
22   8    
23   10    
24   15    
25   17    
26   22    
27   24    
28   29