Data Mining Techniques (4th Edition)

DETAILS : 

• Author: Arun K Pujari
  
• Publisher: ‎ The Orient Blackswan
  
• Publication date: ‎ 1 January 2016
  
• Language: ‎ English
  
• Print length : ‎ 423 pages
  
• ISBN-10: ‎ 9386235056
  
• ISBN-13: ‎ 978-9386235053
  
• Item Weight: ‎650 g

ABOUT THE BOOK

Data Mining Techniques, 4th Edition, authored by the distinguished computer science academician Dr. Arun K Pujari, is a classic, highly regarded textbook that serves as a foundational guide to the theory and algorithmic architecture of data science. Published by Universities Press and distributed globally by Orient Blackswan, this extensively updated fourth edition is engineered specifically for advanced undergraduate and postgraduate students of computer science, data analytics, and information technology. The core philosophy of this text is to bridge the abstract mathematical underpinnings of pattern recognition with the practical software engineering requirements of large-scale database systems. Rather than viewing data mining as a collection of black-box software tools, Dr. Pujari treats the discipline as an algorithmic science, meticulously training readers to understand the internal logic, computational complexity, and optimization constraints of every major data processing technique.

The textbook is structurally organized into highly technical, systematic chapters that gradually advance from data preprocessing fundamentals to cutting-edge machine learning paradigms. It begins with a clear architectural introduction to data warehousing, multi-dimensional data modeling, and online analytical processing (OLAP). The narrative then deepens into a comprehensive exploration of association rule mining, providing a rigorous breakdown of the Apriori and FP-Growth algorithms along with advanced methods for mining sequential patterns. The book features highly structured, mathematically rigorous chapters dedicated to data clustering—covering hierarchical, partitional (K-Means), density-based (DBSCAN), and grid-based methodologies—as well as statistical classification, thoroughly detailing decision trees, Bayesian classifiers, and neural networks. Furthermore, this fourth edition incorporates essential updates on web mining, spatial and temporal data mining, and a robust introduction to big data analytics, all illustrated through step-by-step worked numerical examples and pseudo-code that emphasize conceptual precision.

ABOUT THE AUTHOR

Dr. Arun K Pujari is an elite Indian computer science educator, researcher, and institutional leader who has served in prominent academic positions, including Vice-Chancellor of Central University of Rajasthan and Central University of Odisha, and as a senior professor at the University of Hyderabad. With decades of pioneering contributions to artificial intelligence, machine learning, and data mining, his research work is widely cited in international computing journals.

Dr. Pujari’s instructional style is exceptionally clear, methodical, and pedagogically sound. He addresses the reader with the precision of a veteran university professor, routinely breaking down dense mathematical formulations into intuitive, step-by-step algorithmic workflows. By balancing deep theoretical proofs with clear-cut, practical exercises at the end of each chapter, his literature has remained a definitive and enduring standard for engineering colleges, university classrooms, and technical curriculum frameworks across the subcontinent.