Measures: Their Categorization and computation, 1.9. Dimensions: The role of concept Hierarchiesġ.8. Fact constellations: Schemas for multidimensional Data models, 1.7. Data Cube: A multidimensional data model, Stars, Snowflakesġ.6. Extraction, Transformation, and loading, 1.5. Data warehouse models: Enterprise warehouse, Datamart, and virtual warehouseġ.4. Data Warehousing: A multitier Architecture, 1.3. Please click the download link below to get Data Mining & Data Warehouse quick revision PDF class notes, book, eBook file for BTech Computer Science / IT Engineering syllabus.1.1 Introduction, 1.2. Module, IV - What Is Cluster Analysis, Types of Data in Cluster Analysis,A Categorization of Major Clustering Methods, Classical Partitioning Methods: k-Meansand k-Medoids, Partitioning Methods in Large Databases.
Module, III - What is Classification? What Is Prediction? Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Bayes Theorem, Bayesian Classification, Classification by Back propagation, A Multilayer Feed-Forward Neural Network, Defining a Network Topology.Module, II - Mining Association Rules in Large Databases, Association Rule Mining, Market Basket Analysis: Mining A Road Map, The Apriori Algorithm: Finding Frequent Itemsets Using Candidate Generation,Generating Association Rules from Frequent Itemsets, Improving the Efficiently of Apriori,Mining Frequent Itemsets without Candidate Generation, Multilevel Association Rules.Module, I - Data Mining overview, Data Warehouse and OLAP Technology,Data Warehouse Architecture, Steps for the Design and Construction of Data Warehouses, A Three-Tier Data Warehouse Architecture, OLAP, OLAP queries, metadata repository.
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