Data Mining: Concepts & Techniques

Description

Buy Data Mining: Concepts & Techniques 2nd hand books online, Authored by han, Elsevier in paperback binding from Used Book Store.

This book provides a comprehensive, practical guide to the essential concepts and techniques for extracting maximum value from real business data, suitable for both seasoned professionals and new students of data mining. It includes updates informed by reader feedback and industry changes, with expanded coverage of statistics and machine learning. Features dozens of algorithms and implementation examples presented in easily understood pseudo-code, ready for application in real-world, large-scale data mining projects. The rapid increase in data generation and collection across business, scientific, and government sectors, amplified by technologies like digital cameras, publication tools, bar codes, scanned text and image platforms, satellite remote sensing, and the World Wide Web, has created an urgent demand for automated tools and new techniques to transform this data into actionable information and knowledge. Following the acclaimed first edition, this book delves into concepts and techniques for discovering hidden patterns within large datasets, emphasizing their feasibility, usefulness, effectiveness, and scalability. The new edition significantly builds upon the first, incorporating recent advancements in data mining methods, systems, and applications. New chapters are dedicated to exploring recent developments in mining complex data types, including stream data, sequence data, graph-structured data, social network data, and multi-relational data. This resource is intended for database professionals and researchers, as well as data mining professionals.

About the Author

Jiawei Han is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Well known for his research in the areas of data mining and database systems, he has received many awards for his contributions in the field.

Including the 2004 ACM SIGKDD Innovations Award. He has served as Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data. And on editorial boards of several journals, including IEEE Transactions on Knowledge and Data Engineering and Data Mining and Knowledge Discovery. Micheline Kamber is a researcher with a passion for writing in easy-to-understand terms. She has a master’s degree in computer science (specialising in artificial intelligence) from Concordia University, Canada.

Database Storage & Design
Computer Science
Computing, Internet & Digital Media
9788131205358 ISBN
Han Book Author