Data Mining and Data Warehousing: Principles and Practical Techniques

✍ Scribed by Parteek Bhatia

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

No coin nor oath required. For personal study only.

✦ 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
Front Matter
Data Mining and Data
Warehousing: Principles and Practical Techniques
Copyright
Dedication
Contents
Figures
Tables
Preface
Acknowledgments
1 Beginning with
Machine Learning
2 Introduction to Data Mining
3 Beginning with
Weka and R Language
4 Data Preprocessing
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 Engines
12 Data Warehouse
13 Data Warehouse Schema
14 Online Analytical Processing
15 Big Data and NoSQL
Index
Colour Plates

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