ISBN: 978-81-8487-510-2
E-ISBN: Publication Year: 2016
Pages: 582
Binding: Paper Back Dimension: 185mm x 240mm Weight: 945

Textbook

About the book

DATA MINING METHODS, Second Edition discusses both theoretical foundation and practical applications of data mining in a web field including banking, e-commerce, medicine, engineering and management. This book starts by introducing data and information, basic data type, data category and applications of data mining. The second chapter briefly reviews data visualization technology and importance in data mining. Fundamentals of probability and statistics are discussed in chapter 3, and novel algorithm for sample covariants are derived. The next two chapters give an in-depth and useful discussion of data warehousing and OLAP. Decision trees are clearly explained and a new tabular method for decision tree building is discussed. The chapter on association rules discusses popular algorithms and compares various algorithms in summary table form. An interesting application of genetic algorithm is introduced in the next chapter. Foundations of neural networks are built from scratch and the back propagation algorithm is derived in the appendix. Popular clustering algorithm is discussed in the next chapter. The web mining chapter generalizes the page rank metric in multiple ways. A geometric derivation of SDM appear next and summary table in table form is given. LSI indexing for IRN extension is discussed next. The book ends with a thorough discussion of text mining metrics and gives latest research directions in text mining.

Key Features

• “Application sections” that demonstrate the usefulness of models presented in each chapter
• Large number of URL links to software on the net using which readers can build various data mining models on their own
• Extensive reference section at the end of each chapter, with 300+ research publications cited
• More than 250 exercises (true/false, multiple choice, computer exercises)

Table of Contents

Basic Concepts in Data Mining / Data Visualisation Techniques / Probability and Statistics / Datawarehousing / Online Analytical Processing / Decision Trees / Association Rules / Regression / Cluster Analysis / Genetic Algorithms / Neural Networks / Web Mining / Support Vector Machines / Latent Semantic Indexing / Text Mining / Appendixes / Solutions to Selected Exercises / Index / Subject Index.

Audience

Senior Undergraduate Students in Computer Science, Computer Engineering,
Information Technology, Management and Statistics