Data Mining and Data Warehousing
Principles and Practical Techniques

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Parteek Bhatia (Author)

9781108727747, Cambridge University Press

Paperback / softback, published 27 June 2019

506 pages
24.1 x 18.3 x 2 cm, 0.66 kg

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.

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.

Subject Areas: Pattern recognition [UYQP], Machine learning [UYQM]