Data Warehousing & Data Mining : Express Learning

Data Warehousing And Data Mining Is Presented In A Question-and-answer Format Following The Examination Pattern And Covers All Key Topics In The Syllabus. The Book Is Designed To Make Learning Fast And Effective And Is Precise, Up-to-date And Will Help Students Excel In Their Examinations. The Book Is Part Of The Express Learning Is A Series Of Books Designed As Quick Reference Guides To Important Undergraduate Courses. The Organized And Accessible Format Of These Books Allows Students To Learn Important Concepts In An Easy-to-understand, Question-and-answer Format. These Portable Learning Tools Have Been Designed As One-stop References For Students To Understand And Master The Subjects By Themselves. Cover Contents Preface Chapter 1: Introduction to Data Warehouse For Using the Data Warehouse For Building the Data Warehouse For Administering the Data Warehouse Business Metadata Technical Metadata Reporting and Managed Query Tools OLAP Tools Application Development Tools Data Mining Tools Data Visualization Tools First Generation Client/Server Model Second Generation Client/Server Model Multiple Choice Questions Answers Chapter 2: Building a Data Warehouse Business Considerations Design Considerations Technical Considerations Implementation Considerations Data Partitioning Data Clustering Parallel Processing Summary Levels Multiple Choice Questions Answers Chapter 3: Data Warehouse: Architecture Compute Cube Operator Partial Materialization Star Schema Snowflake Schema Fact Constellation Schema Multiple Choice Questions Answers Chapter 4: OLAP Technology MOLAP Architecture Data Design and Preparation Administration Performance OLAP Platforms OLAP Tools and Products Implementation Steps Indexing OLAP Data Processing of OLAP queries Multiple Choice Questions Answers Chapter 5: Introduction to Data Mining Class/Concept Description Mining Frequent Patterns, Associations and Correlations Classification and Prediction Cluster Analysis Outlier Analysis Evolution Analysis On the Basis of Prediction/Description On the Basis of Automatic/Manual Mining of Data Multiple Choice Questions Answers Chapter 6: Data Preprocessing Wavelet Transforms Principal Components Analysis (PCA) Regression Log-Linear Models Histograms Clustering Sampling Input Output Procedure Explanation Multiple Choice Questions Answers Chapter 7: Mining Association Rules Generalized Association Rules Multi-level Association Rules Multidimensional Association Rules Multiple Choice Questions Answers Chapter 8: Classification and Prediction Naive Bayesian Bayesian Belief Network Linear Regression Non-linear Regression Bagging Boosting Multiple Choice Questions Answers Chapter 9: Cluster Analysis Statistical Distribution-based Outlier Detection Distance-based Outlier Detection Density-based Local Outlier Detection Deviation-based Outlier Detection Multiple Choice Questions Answers Chapter 10: Advanced Techniques of Data Mining and Its Applications Financial Data Analysis Retail Industry Intrusion Detection Telecommunication Industry Multiple Choice Questions Answers Index