Book contents
- Frontmatter
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgments
- 1 Beginning with Machine Learning
- 2 Introduction to Data Mining
- 3 Beginning with Weka and R Language
- 4 Data Preprocessing
- 5 Classification
- 6 Implementing Classification in Weka and R
- 7 Cluster Analysis
- 8 Implementing Clustering with Weka and R
- 9 Association Mining
- 10 Implementing Association Mining with Weka and R
- 11 Web Mining and Search Engines
- 12 Data Warehouse
- 13 Data Warehouse Schema
- 14 Online Analytical Processing
- 15 Big Data and NoSQL
- Index
- Colour Plates
Frontmatter
Published online by Cambridge University Press: 26 April 2019
- Frontmatter
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgments
- 1 Beginning with Machine Learning
- 2 Introduction to Data Mining
- 3 Beginning with Weka and R Language
- 4 Data Preprocessing
- 5 Classification
- 6 Implementing Classification in Weka and R
- 7 Cluster Analysis
- 8 Implementing Clustering with Weka and R
- 9 Association Mining
- 10 Implementing Association Mining with Weka and R
- 11 Web Mining and Search Engines
- 12 Data Warehouse
- 13 Data Warehouse Schema
- 14 Online Analytical Processing
- 15 Big Data and NoSQL
- Index
- Colour Plates
Summary
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- Type
- Chapter
- Information
- Data Mining and Data WarehousingPrinciples and Practical Techniques, pp. i - ivPublisher: Cambridge University PressPrint publication year: 2019