Data Mining Concepts and Techniques (Second Edition)

Author: (Canada) Han Jia Wei et al.
Publisher:
Publish Date: 2006-04-01
Features: Our ability to generate and collect data is growing rapidly. In addition to the increasing computerization of most business, scientific, and government activities generating data, the widespread use of digital cameras, publishing tools, and barcodes also generates data. In terms of data collection, scanned text and image platforms, satellite remote sensing systems, and the international internet have surrounded our lives with massive amounts of data. This explosive growth in data has made it more urgent than ever before that we need new technologies and automated tools to help us convert this data into useful information and knowledge. The first edition of this book was voted the most popular data mining textbook by readers of KDnuggets and is an excellent readable textbook. It comprehensively and systematically introduces the basic concepts, methods, and techniques of data mining, as well as the research progress in data mining technology, with a focus on its feasibility, usefulness, effectiveness, and scalability issues. However, since the first edition was published, research in the field of data mining has made significant progress, and new data mining methods, systems, and applications have been developed. The second edition has strengthened this aspect by adding multiple chapters on new data mining methods to enable the mining of complex types of data, including stream data, sequence data, graph-structured data, social network data, and multi-relational data. This book is suitable as a selective course textbook for senior undergraduate students in computer science and related fields, particularly as a specialized textbook for graduate students. It can also serve as a reference book for professionals engaged in data mining research and application development. The main features of this book are: comprehensively and practically discussing the concepts and techniques that readers need to know from real-world business data. It has been updated and integrated based on feedback from readers, technological changes in the field of data mining, and more materials from statistics and machine learning. It includes many algorithms and practical examples, all written in easy-to-understand pseudocode, suitable for large-scale data mining projects.