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# class08 - Chapter6. ?Whatis prediction(SVM andprediction...

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August 29, 2011 Data Mining: Concepts and Techniques 1 Chapter 6. Classification and Prediction What is classification? What is  prediction? Issues regarding classification  and prediction Classification by decision tree  induction Bayesian classification Rule-based classification Classification by back  propagation Support Vector Machines  (SVM)  Associative classification  Lazy learners (or learning from  your neighbors) Other classification methods Prediction Accuracy and error measures Ensemble methods Model selection

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August 29, 2011 Data Mining: Concepts and Techniques 2 Nearest Neighbor Classifiers Basic idea: If it walks like a duck, quacks like a duck, then  it s probably a duck Training Records Test Record Compute Distance Choose k of the “nearest” records
August 29, 2011 Data Mining: Concepts and Techniques 3 Nearest neighbor method Majority vote within the k nearest neighbors     K= 1: brown K= 3: green new

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August 29, 2011 Data Mining: Concepts and Techniques 4 Nearest-Neighbor Classifiers Requires three things The set of stored records Distance Metric to compute distance between records The value of k , the number of nearest neighbors to retrieve To classify an unknown record: Compute distance to other training records Identify k nearest neighbors Use class labels of nearest neighbors to determine the class label of unknown record (e.g., by taking majority vote) Unknown record
August 29, 2011 Data Mining: Concepts and Techniques 5 Definition of Nearest Neighbor X X X (a) 1-nearest neighbor (b) 2-nearest neighbor (c) 3-nearest neighbor K-nearest neighbors of a record x are data points that have the k smallest distance to x

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August 29, 2011 Data Mining: Concepts and Techniques 6 1 nearest-neighbor Voronoi Diagram
August 29, 2011 Data Mining: Concepts and Techniques 7 Nearest Neighbor Classification Compute distance between two points: Euclidean distance  Determine the class from nearest neighbor list take the majority vote of class labels among the  k-nearest neighbors Weigh the vote according to distance  weight factor, w = 1/d 2 - = i i i q p q p d 2 ) ( ) , (

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August 29, 2011 Data Mining: Concepts and Techniques 8 Nearest Neighbor Classification… Choosing the value of k: If k is too small, sensitive to noise points If k is too large, neighborhood may include points from  other classes X
August 29, 2011 Data Mining: Concepts and Techniques 9 Nearest Neighbor Classification… Scaling issues Attributes may have to be scaled to prevent  distance measures from being dominated by  one of the attributes Example:  height of a person may vary from 1.5m to 1.8m  weight of a person may vary from 90lb to 300lb  income of a person may vary from \$10K to \$1M

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class08 - Chapter6. ?Whatis prediction(SVM andprediction...

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