1. Algorithms: The Basic Methods Witten – Chapter 4 Charles Tappert Professor of Computer Science School of CSIS, Pace University

2. 1. Inferring Rudimentary Rules 1R (1-rule) Method This method tests a single attribute and creates a rule that assigns the most frequent class to that attribute

3. 2. Statistical Modeling Naïve Bayes Method Assumes statistical independence – multiply probabilities

4. 2. Statistical Modeling Naïve Bayes Method

5. 3. Divide-and-Conquer: Construct Decision Trees: ID3 Method

8. Compare: Example from Naïve Bayes Method

9. 4. Covering Algorithms: Constructing Rules

10. 5. Mining Association Rules

12. 6. Linear Models Prediction by linear regression

13. 6. Linear Models Linear Classification via Perceptron

14. Non-parametric algorithm 7. Instance-Based Learning k-nearest-neighbor method

15. 8. Clustering: k-means Technique Top down method Specify in advance number of clusters, k Randomly choose k seed points Find the closest points to the seed points Compute the means of points closest to each seed point –> seeds for next iteration Stop when the seed points become stable

16. 8. Clustering: k-means Technique Top down method

17. Clustering: Hierarchy - Dendrogram Bottom up method Also, see Witten p 81, p 275-278 Mary Manfredi dissertation