Data Mining: Concepts and Techniques Second Edition Jiawei Han and Micheline Kamber Our ability to generate and collect data has been increasing rapidly. In addition to the computerization of most business, scientific and government transactions, the widespread use of digital cameras, publication tools, and bar codes also generated data. On the collections side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KDnuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data - including stream data, sequence data, graph structured data, social networks data, and multi-relational data. Whether you are a seasoned professional or a new student of data mining, this book has much to offer you: * a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data. * updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning. * dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large scale data mining projects. * complete classroom support for instructors at www.mkp.com/datamining2e. Jiawei Han is 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 […] Micheline Kamber is a researcher with a passion for writing in easing-to-understand terms. She has a mater's degree in Computer Science from Concordia University, Canada.