and classes as strings, Tree-based representations, Frequentitemsets for representing classes and clusters, Patterns and classes as logical formulas. Proximity Measures- Dissimilarity measures, metrics, similarity measures, UNIT 3 Edit distance, Hausdorff metric between point sets, Kernel functions, Contextual and conceptual similarity between points. Dimensionality Reduction- Feature selection: Branch and bound, Sequential feature selection, Feature extraction: Fisher's linear discriminant, Principal components as features; Nearest Neighbor Classifiers- Nearest neighbor UNIT 4 classifier, Soft nearest neighbor classifiers, Efficient algorithms for nearest neighbor classification, K-nearest neighbor classifier, minimal distance classifier, condensed nearest neighbor classifier and its modifications. Bayes Classifier- Bayes classifier, naïve Bayes classifier, Belief net; UNIT 5 Decision Trees- Axis-parallel and oblique decision trees, Learning decision trees, Information gain and Impurity measures. OUTCOMES: : At the end of the course, the student should be able to: Concepts of Pattern Identification in Data Mining. Understanding algorithms for Classification. Use of Classifiers. MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES: H = Highly Related; M = Medium L = Low Text Books: 1.V. Susheela Devi and M. NarasimhaMurty, Universities Pattern Recognition: An Introduction, Press,Hyderabad, 2011. Reference Books: 1.R. O. Duda, P. E. Hart and D. G. Stork, John Wiley and Sons, 2000. Pattern Classification, 2.M. NarasimhaMurty and V. Susheela Devi, NPTEL Web Course, 2011 Pattern Recognition, ().
and classes as strings, Tree-based representations, Frequentitemsets for representing classes and clusters, Patterns and classes as logical formulas. UNIT 3 Proximity Measures- Dissimilarity measures, metrics, similarity measures, Edit distance, Hausdorff metric between point sets, Kernel functions, Contextual and conceptual similarity between points. UNIT 4 Dimensionality Reduction- Feature selection: Branch and bound, Sequential feature selection, Feature extraction: Fisher's linear discriminant, Principal components as features; Nearest Neighbor Classifiers- Nearest neighbor classifier, Soft nearest neighbor classifiers, Efficient algorithms for nearest neighbor classification, K-nearest neighbor classifier, minimal distance classifier, condensed nearest neighbor classifier and its modifications. UNIT 5 Bayes Classifier- Bayes classifier, naïve Bayes classifier, Belief net; Decision Trees- Axis-parallel and oblique decision trees, Learning decision trees, Information gain and Impurity measures. OUTCOMES: : At the end of the course, the student should be able to: Concepts of Pattern Identification in Data Mining. Understanding algorithms for Classification. Use of Classifiers. MAPPING COURSE OUTCOMES LEADING TO THE ACHIEVEMENT OF PROGRAM OUTCOMES AND PROGRAM SPECIFIC OUTCOMES: H = Highly Related; M = Medium L = Low Text Books: 1.V. Susheela Devi and M. NarasimhaMurty, Pattern Recognition: An Introduction, Universities Press,Hyderabad, 2011. Reference Books: 1.R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, John Wiley and Sons, 2000. 2.M. NarasimhaMurty and V. Susheela Devi, Pattern Recognition, NPTEL Web Course, 2011 ().