Data Mining and Data Warehousing: Principles and Practical Techniques

Description

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. 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