DATA MINING AND WAREHOUSING MODULE: I Unit 1- Fundamentals of Data Mining: Defnition, Motivation, what kinds of data? Data Mining Functionalities Unit 2 - Classification of Data Mining Systems, Major Issues in Data Mining. Unit 3 - Data Warehouse and OLAP Technology for Data Mining: Definition, Multidimensional datamodels, Data Warehouse Architecture Unit 4 - Data Warehouse Implementation, Further development of data cube technology, From Data Warehousing to Data Mining. MODULE: II Unit 1 - Data Preprocessing: Need for preprocess the data, Data Cleaning, Data Integration, Data Transformation Unit 2 - Data Reduction, Discretization and Concept of Hierarchy Generation. Unit 3 - Data Mining Primitives, Languages and system Architectures: Data Mining Primitives, Architectures of Data Mining System. Unit 4 - A Data Mining Query Language, Designing Graphical User Interfaces Based on a Data Mining Query Language MODULE: III Unit 1 - Concept Description-Characterization: Definition, Data Generalization and Summarization-Based Characterization, Analytical Characterization Unit 2 - Concept Description-Comparison: Mining Class Comparisons, Mining Descriptive Statistical Measures in Large Databases Unit 3 - Mining Association Rules in Large Databases: Association Rule Mining, Mining Single–Dimensional Boolean Association Rules form Transactional Databases Unit 4 - Mining Multilevel Association Rules form Transaction Databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses. MODULE: IV Unit 1 - Classification and Prediction: Definition, Issues Regarding Classification and Prediction Unit 2 - Classification by Decision Tree Induction, Bayesian Classification, Classification by Back propagation Unit 3 - Classification Based on Concepts form Association Rule Mining, Other Classification Methods – Prediction, Classifier Accuracy. MODULE: V Unit 1 – Cluster Analysis: Definition, Types of data, Clustering methods Unit 2 - Partitioning methods, Hierarchical methods, Density based methods Unit 3 – Grid based methods, Model based clustering methods, Outlier analysis Module: VI Unit 1 - Mining Complex Types of Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Mining Spatial Databases Unit 2 - Mining Multimedia Databases, Mining Time – Series and Sequence Data , Mining Text Databases, Mining the World Wide Web. Unit 3 - Applications and Trends in Data Mining: Data Mining Applications, Data Mining /system Products and Research Prototypes Unit 4 - Additional Themes on Data Mining, Social Impacts of /data Mining, Trends in /data Mining.
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