Data Mining and Data Warehousing: Principles and Practical Techniques

✍ Scribed by Parteek Bhatia

• Publisher: Cambridge University Press
• Year: 2019
• Tongue: English
• Leaves: 513
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover......Page 1
Front Matter
......Page 3
Data Mining and Data
Warehousing: Principles and Practical Techniques......Page 5
Copyright
......Page 6
Dedication
......Page 7
Contents
......Page 9
Figures......Page 17
Tables......Page 27
Preface......Page 33
Acknowledgments......Page 35
1 Beginning with
Machine Learning......Page 37
2 Introduction to Data Mining......Page 53
3 Beginning with
Weka and R Language......Page 64
4 Data Preprocessing......Page 91
5 Classification......Page 101
6 Implementing
Classification in Weka and R......Page 164
7 Cluster Analysis......Page 191
8 Implementing
Clustering with Weka and R......Page 242
9 Association Mining......Page 265
10 Implementing Association
Mining with Weka and R......Page 355
11 Web Mining
and Search Engines......Page 404
12 Data Warehouse......Page 424
13 Data Warehouse Schema......Page 441
14 Online Analytical Processing......Page 452
15 Big Data and NoSQL......Page 478
Index......Page 503
Colour Plates......Page 505

✦ Subjects

Data mining, Data Warehouse, Weka,