| Description |
1 online resource |
| Contents |
Cover13; -- Copyright -- Dedication -- Preface -- Acknowledgement -- Contents -- Chapter 1 Data Mining and Warehousing Concepts -- 1.1 Introduction13; -- 1.2 Data Mining Definitions13; -- 1.3 Data Mining Tools13; -- 1.4 Applications of Data Mining13; -- 1.5 Data Warehousing and Characteristics13; -- 1.6 Data Warehouse Architecture13; -- Exercise13; -- Chapter 2 Learning and Types of Knowledge -- 2.1 Introduction13; -- 2.2 What is Learning?13; -- 2.3 Anatomy of Data Mining13; -- 2.4 Different Types of Knowledge13; -- Exercise13; -- Chapter 3 Knowledge Discovery Process -- 3.1 Introduction13; -- 3.2 Evaluation of Data Mining13; -- 3.3 Stages of the Data Mining Process13; -- 3.4 Data Mining Operations13; -- 3.5 Architecture of Data Mining13; -- Exercise13; -- Chapter 4 Data Mining Techniques -- 4.1 Introduction13; -- 4.2 Classification13; -- 4.3 Neural Networks13; -- 4.4 Decision Trees13; -- 4.5 Genetic Algorithm13; -- 4.6 Clustering13; -- 4.7 Online Analytic Processing (OLAP)13; -- 4.8 Association Rules13; -- 4.9 Emerging Trends in Data Mining13; -- 4.10 Data Mining Research Projects13; -- Exercise13; -- Chapter 5 Real Time Applications and Future Scope -- 5.1 Applications of Data Mining13; -- 5.2 Future Scope13; -- 5.3 Data Mining Products13; -- Exercise13; -- Chapter 6 Data Warehouse Evaluation -- 6.1 The Calculations for Memory Capacity13; -- 6.2 Data, Information and Knowledge13; -- 6.3 Fundamental of Database13; -- 6.4 OLAP And OLAP Server13; -- 6.5 Data Warehouses, OLTP, OLAP and Data Mining13; -- Exercise13; -- Chapter 7 Data Warehouse Design -- 7.1 Introduction13; -- 7.2 The Central Data Warehouse13; -- 7.3 Data Warehousing Objects13; -- 7.4 Goals of Data Warehouse Architecture13; -- 7.5 Data Warehouse Users13; -- 7.6 Design the Relational Database and OLAP Cubes13; -- 7.7 Data Warehousing Schemas13; -- Exercise13; -- Chapter 8 Partitioning in Data Warehouse -- 8.1 Introduction13; -- 8.2 Hardware Partitioning13; -- 8.3 RAID Levels13; -- 8.4 Software Partitioning Methods13; -- Exercise13; -- Chapter 9 Data Mart and Meta Data -- 9.1 Introduction13; -- 9.2 Data Mart13; -- 9.3 Meta Data13; -- 9.4 Legacy Systems13; -- Exercise13; -- Chapter 10 Backup and Recovery of the Data Warehouse -- 10.1 Introduction13; -- 10.2 Types of Backup13; -- 10.3 Backup the Data Warehouse13; -- 10.4 Data Warehouse Recovery Models13; -- Exercise13; -- Chapter 11 Performance Tuning and Future of data Warehouse -- 11.1 Introduction13; -- 11.2 Prioritized Tuning Steps13; -- 11.3 Challenges of the Data Warehouse13; -- 11.4 Benefits of Data Warehousing13; -- 11.5 Future of the Data Warehouse13; -- 11.6 New Architecture of Data Warehouse13; -- Exercise13; -- Appendix A Glossary -- Appendix B Multiple Choice Questions13; -- Appendix C Questions & Answers13; -- Appendix D Model Question Papers13; -- Bibliography -- Index |
| Summary |
Data Mining is the process of analyzing large amount of data in search of previously undiscovered business patterns. Data Warehousing is a relational/multidimensional database that is designed for Query and Analysis rather than Transaction Processing. This book provides a systematic introduction to the principles of Data Mining and Data Warehousing. It covers the entire range of data mining algorithms (prediction, classification, and association), data mining products and applications, stages |
| Notes |
Title from PDF title page (viewed February 25, 2009) |
| Bibliography |
Includes bibliographical references (pages 125-126) and index |
| Subject |
Data mining.
|
|
Data warehousing.
|
|
Data Mining
|
|
COMPUTERS -- Database Management -- Data Mining.
|
|
Data mining
|
|
Data warehousing
|
| Form |
Electronic book
|
| Author |
Venatesan, N
|
| ISBN |
9788122424324 |
|
8122424325 |
|
1281788872 |
|
9781281788870 |
|
8122419720 |
|
9788122419726 |
|
