Data Mining and Warehousing.

By: Prabhu, SContributor(s): Venkatesan, NMaterial type: TextTextPublisher: Daryaganj : New Age International, 2006Copyright date: ©2007Edition: 1st edDescription: 1 online resource (144 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9788122424324Subject(s): Data mining | Data warehousingGenre/Form: Electronic books.Additional physical formats: Print version:: Data Mining and WarehousingDDC classification: 005.74 LOC classification: QA76.9.D343 -- P73 2007ebOnline resources: Click to View
Contents:
Cover -- Preface -- Contents -- Chapter 1 Data Mining and Warehousing Concepts -- 1.1 Introduction -- 1.2 Data Mining Definitions -- 1.3 Data Mining Tools -- 1.4 Applications of Data Mining -- 1.5 Data Warehousing and Characteristics -- 1.6 Data Warehouse Architecture -- Exercise -- Chapter 2 Learning and Types of Knowledge -- 2.1 Introduction -- 2.2 What is Learning? -- 2.3 Anatomy of Data Mining -- 2.4 Different Types of Knowledge -- Exercise -- Chapter 3 Knowledge Discovery Process -- 3.1 Introduction -- 3.2 Evaluation of Data Mining -- 3.3 Stages of the Data Mining Process -- 3.4 Data Mining Operations -- 3.5 Architecture of Data Mining -- Exercise -- Chapter 4 Data Mining Techniques -- 4.1 Introduction -- 4.2 Classification -- 4.3 Neural Networks -- 4.4 Decision Trees -- 4.5 Genetic Algorithm -- 4.6 Clustering -- 4.7 Online Analytic Processing (OLAP) -- 4.8 Association Rules -- 4.9 Emerging Trends in Data Mining -- 4.10 Data Mining Research Projects -- Exercise -- Chapter 5 Real Time Applications and Future Scope -- 5.1 Applications of Data Mining -- 5.2 Future Scope -- 5.3 Data Mining Products -- Exercise -- Chapter 6 Data Warehouse Evaluation -- 6.1 The Calculations for Memory Capacity -- 6.2 Data, Information and Knowledge -- 6.3 Fundamental of Database -- 6.4 OLAP And OLAP Server -- 6.5 Data Warehouses, OLTP, OLAP and Data Mining -- Exercise -- Chapter 7 Data Warehouse Design -- 7.1 Introduction -- 7.2 The Central Data Warehouse -- 7.3 Data Warehousing Objects -- 7.4 Goals of Data Warehouse Architecture -- 7.5 Data Warehouse Users -- 7.6 Design the Relational Database and OLAP Cubes -- 7.7 Data Warehousing Schemas -- Exercise -- Chapter 8 Partitioning in Data Warehouse -- 8.1 Introduction -- 8.2 Hardware Partitioning -- 8.3 RAID Levels -- 8.4 Software Partitioning Methods -- Exercise -- Chapter 9 Data Mart and Meta Data.
9.1 Introduction -- 9.2 Data Mart -- 9.3 Meta Data -- 9.4 Legacy Systems -- Exercise -- Chapter 10 Backup and Recovery of the Data Warehouse -- 10.1 Introduction -- 10.2 Types of Backup -- 10.3 Backup the Data Warehouse -- 10.4 Data Warehouse Recovery Models -- Exercise -- Chapter 11 Performance Tuning and Future of data Warehouse -- 11.1 Introduction -- 11.2 Prioritized Tuning Steps -- 11.3 Challenges of the Data Warehouse -- 11.4 Benefits of Data Warehousing -- 11.5 Future of the Data Warehouse -- 11.6 New Architecture of Data Warehouse -- Exercise -- Appendix A Glossary -- Appendix B Multiple Choice Questions -- Appendix C Questions & Answers -- Appendix D Model Question Papers -- 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.
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Cover -- Preface -- Contents -- Chapter 1 Data Mining and Warehousing Concepts -- 1.1 Introduction -- 1.2 Data Mining Definitions -- 1.3 Data Mining Tools -- 1.4 Applications of Data Mining -- 1.5 Data Warehousing and Characteristics -- 1.6 Data Warehouse Architecture -- Exercise -- Chapter 2 Learning and Types of Knowledge -- 2.1 Introduction -- 2.2 What is Learning? -- 2.3 Anatomy of Data Mining -- 2.4 Different Types of Knowledge -- Exercise -- Chapter 3 Knowledge Discovery Process -- 3.1 Introduction -- 3.2 Evaluation of Data Mining -- 3.3 Stages of the Data Mining Process -- 3.4 Data Mining Operations -- 3.5 Architecture of Data Mining -- Exercise -- Chapter 4 Data Mining Techniques -- 4.1 Introduction -- 4.2 Classification -- 4.3 Neural Networks -- 4.4 Decision Trees -- 4.5 Genetic Algorithm -- 4.6 Clustering -- 4.7 Online Analytic Processing (OLAP) -- 4.8 Association Rules -- 4.9 Emerging Trends in Data Mining -- 4.10 Data Mining Research Projects -- Exercise -- Chapter 5 Real Time Applications and Future Scope -- 5.1 Applications of Data Mining -- 5.2 Future Scope -- 5.3 Data Mining Products -- Exercise -- Chapter 6 Data Warehouse Evaluation -- 6.1 The Calculations for Memory Capacity -- 6.2 Data, Information and Knowledge -- 6.3 Fundamental of Database -- 6.4 OLAP And OLAP Server -- 6.5 Data Warehouses, OLTP, OLAP and Data Mining -- Exercise -- Chapter 7 Data Warehouse Design -- 7.1 Introduction -- 7.2 The Central Data Warehouse -- 7.3 Data Warehousing Objects -- 7.4 Goals of Data Warehouse Architecture -- 7.5 Data Warehouse Users -- 7.6 Design the Relational Database and OLAP Cubes -- 7.7 Data Warehousing Schemas -- Exercise -- Chapter 8 Partitioning in Data Warehouse -- 8.1 Introduction -- 8.2 Hardware Partitioning -- 8.3 RAID Levels -- 8.4 Software Partitioning Methods -- Exercise -- Chapter 9 Data Mart and Meta Data.

9.1 Introduction -- 9.2 Data Mart -- 9.3 Meta Data -- 9.4 Legacy Systems -- Exercise -- Chapter 10 Backup and Recovery of the Data Warehouse -- 10.1 Introduction -- 10.2 Types of Backup -- 10.3 Backup the Data Warehouse -- 10.4 Data Warehouse Recovery Models -- Exercise -- Chapter 11 Performance Tuning and Future of data Warehouse -- 11.1 Introduction -- 11.2 Prioritized Tuning Steps -- 11.3 Challenges of the Data Warehouse -- 11.4 Benefits of Data Warehousing -- 11.5 Future of the Data Warehouse -- 11.6 New Architecture of Data Warehouse -- Exercise -- Appendix A Glossary -- Appendix B Multiple Choice Questions -- Appendix C Questions & Answers -- Appendix D Model Question Papers -- Bibliography -- Index.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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