Data Mining and Data Warehousing
eBook - PDF

Data Mining and Data Warehousing

Principles and Practical Techniques

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Data Mining and Data Warehousing

Principles and Practical Techniques

About this book

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.

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Yes, you can access Data Mining and Data Warehousing by Parteek Bhatia in PDF and/or ePUB format, as well as other popular books in Computer Science & Databases. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Data Mining and Data Warehousing
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. Figures
  8. Tables
  9. Preface
  10. Acknowledgments
  11. 1. Beginning with Machine Learning
  12. 2. Introduction to Data Mining
  13. 3. Beginning with Weka and R Language
  14. 4. Data Preprocessing
  15. 5. Classification
  16. 6. Implementing Classification in Weka and R
  17. 7. Cluster Analysis
  18. 8. Implementing Clustering with Weka and R
  19. 9. Association Mining
  20. 10. Implementing Association Mining with Weka and R
  21. 11. Web Mining and Search Engines
  22. 12. Data Warehouse
  23. 13. Data Warehouse Schema
  24. 14. Online Analytical Processing
  25. 15. Big Data and NoSQL
  26. Index
  27. Colour Plates