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Algorithms: The Basic Methods Witten – Chapter 4

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.

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Algorithms: The Basic Methods Witten – Chapter 4

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

  2. 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

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

  4. 2. Statistical ModelingNaïve Bayes Method

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

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

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

  8. Compare: Example from Naïve Bayes Method 3. Divide-and-Conquer:Construct Decision Trees: ID3 Method

  9. 4. Covering Algorithms: Constructing Rules

  10. 5. Mining Association Rules

  11. 5. Mining Association Rules

  12. 6. Linear ModelsPrediction by linear regression

  13. 6. Linear ModelsLinear Classification via Perceptron

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

  15. 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

  16. 8. Clustering: k-means TechniqueTop down method

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

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