Scope & Objectives Data mining process – Data mining functionalities – Data preprocessing, Supervised Learning: Introduction, Decision Tree Induction, Bayesian Classification: Naïve Bayes. Rule Based Classification, Artificial Neural Network: Classification by Back propagation. Support Vector Machines, Associative Classification, K-NN classifier, Case-Based Learning, Rough set, Fuzzy set approaches, Hidden Markov models. Ensemble model: Bagging, Boosting. Accuracy and Error Measures, Evaluating the Accuracy of a Classifier, Model Selection, Feature selection. Unsupervised Learning-I: Types of Data in Cluster Analysis, Clustering Methods- Partitioning Methods: K-Means, Fuzzy Clustering Methods: FCM, PCM, FPCM, PFCM. Hierarchical Methods: Agglomerative and Divisive, Balanced Iterative Reducing Clustering using Hierarchies, Unsupervised Learning-II: Grid-Based Methods: Statistical Information Grid, Model-Based Clustering Methods: EM algorithm, Self Organizing Map. Clustering High-Dimensional Data: Clustering In Quest, Projective clustering, Outlier Analysis. Soft Computing
Components: Neighborhood based algorithms-Simulated annealing, Tabu search. Population based algorithms Evolutionary computation: Evolutionary programming and strategies, Genetic algorithms, Differential evolution. Swarm Intelligence: Ant colony optimization, Particle swarm optimization