Data Mining and Data Warehousing: Principles and Practical Techniques

by Parteek Bhatia

Overview

Written in lucid language, this valuable textbook brings together fundamental concepts of data mining and data warehousing in a single volume. Important topics including information theory, decision tree, Naïve Bayes classifier, distance metrics, partitioning clustering, associate mining, data marts and operational data store are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of computer science, engineering and information technology for a course on data mining and data warehousing. The text simplifies the understanding of the concepts through exercises and practical examples. Chapters such as classification, associate mining and cluster analysis are discussed in detail with their practical implementation using Weka and R language data mining tools. Advanced topics including big data analytics, relational data models and NoSQL are discussed in detail. Pedagogical features including unsolved problems and multiple-choice questions are interspersed throughout the book for better understanding.

Product Details

ISBN-13:	9781108727747
Publisher:	Cambridge University Press
Publication date:	06/27/2019
Pages:	506
Product dimensions:	7.20(w) x 9.49(h) x 0.79(d)

About the Author

Parteek Bhatia is an associate professor in the department of computer science and engineering at Thapar Institute of Engineering and Technology, Patiala. He has more than twenty years of teaching experience and has published papers in journals. His current research includes natural language processing, machine learning and human computer interface. He has taught courses including data mining and data warehousing, big data analysis and database management system at undergraduate and graduate levels.

Table of Contents

Preface; Acknowledgement; Dedication; 1. Beginning with machine learning; 2. Introduction to data mining; 3. Beginning with Weka and R language; 4. Data pre-processing; 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 engine; 12. Operational data store and data warehouse; 13. Data warehouse schema; 14. Online analytical processing; 15. Big data and NoSQL; Reference; Index.