1. Data Warehousing : OLAP and OLTP, Data warehouse and Data mart, OLAM architecture, Extraction, Transform and Loading (ETL) concept for generic, two-tier, three - tier architecture, Data warehousing schema - Star, Snowflake, Fact Constellation (Galaxy) - Data Cube , Operations on Data cube (slicing, roll up, roll down, drill up etc). (Chapter - 1) 2. 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 - 2) 3. 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 - 3) 4. Mining Frequent Patterns, Associations and Correlations : 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 - 4) 5. 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 - 5) 6. 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 Hierarchical Clustering, Strengths and Weakness; Outlier Detection, Clustering high dimensional data, clustering Graph and Network data. (Chapter - 6) 7. Advance topics : Introduction to Web Mining, Spatial Data Mining, Temporal Mining, Text Mining and Multimedia Mining. (Chapter - 7)
