Data Mining for GTU 18 Course (VI-CE/ CSE/Prof. Elec.-II - 3160714)  (Paperback, I. A. Dhotre)

1. Introduction to data mining (DM) : Motivation for Data Mining - Data Mining-Definition and Functionalities - Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM - KDD Process. (Chapter - 1) 2. Data Pre-processing : Data summarization, data cleaning, data integration and transformation, data reduction, data discretization and concept hierarchy generation, feature extraction, feature transformation, feature selection, introduction to Dimensionality Reduction, CUR decomposition. (Chapter - 2) 3. Concept Description, Mining Frequent Patterns, Associations and Correlations : What is concept description ? - Data Generalization and summarization - based characterization - Attribute relevance - class comparisons, Basic concept, efficient and scalable frequent item-set mining methods, mining various kind of association rules, from association mining to correlation analysis, Advanced Association Rule Techniques, Measuring the Quality of Rules. (Chapter - 3) 4. Classification and Prediction : Classification vs. prediction, Issues regarding classification and prediction, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms, Neural Network-Based Algorithms, Rule-Based Algorithms, Combining Techniques, accuracy and error measures, evaluation of the accuracy of a classifier or predictor. Neural Network Prediction methods : Linear and nonlinear regression, Logistic Regression Introduction of tools such as DB Miner / WEKA / DTREG DM Tools. (Chapter - 4) 5. Cluster Analysis : Clustering : Problem Definition, Clustering Overview, Evaluation of Clustering Algorithms, Partitioning Clustering - K - Means Algorithm, K - Means Additional issues, PAM Algorithm; Hierarchical Clustering - Agglomerative Methods and divisive methods, Basic Agglomerative. 6. Web mining and other data mining :