algorithms: the basic methods witten – chapter 4
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Algorithms: The Basic Methods Witten – Chapter 4. Charles Tappert Professor of Computer Science School of CSIS, Pace University. 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. - PowerPoint PPT PresentationTRANSCRIPT
Algorithms: The Basic MethodsWitten – Chapter 4
Charles Tappert Professor of Computer ScienceSchool of CSIS, Pace University
1. Inferring Rudimentary Rules1R (1-rule) Method
This method tests a single attribute and creates a rule that assigns the most frequent class to that attribute
2. Statistical ModelingNaïve Bayes Method
Assumes statistical independence – multiply probabilities
2. Statistical ModelingNaïve Bayes Method
3. Divide-and-Conquer:Construct Decision Trees: ID3 Method
3. Divide-and-Conquer:Construct Decision Trees: ID3 Method
3. Divide-and-Conquer:Construct Decision Trees: ID3 Method
3. Divide-and-Conquer:Construct Decision Trees: ID3 Method
Compare: Example from Naïve Bayes Method
4. Covering Algorithms: Constructing Rules
5. Mining Association Rules
5. Mining Association Rules
6. Linear ModelsPrediction by linear regression
6. Linear ModelsLinear Classification via Perceptron
Non-parametric algorithm
7. Instance-Based Learningk-nearest-neighbor method
8. Clustering: k-means TechniqueTop 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
8. Clustering: k-means TechniqueTop down method
Clustering: Hierarchy - DendrogramBottom up method
Also, see Witten p 81, p 275-278
Mary Manfredi dissertation