Relational Data Clustering - Models, Algorithms, and Applications

Informationen zum Autor Bo Long is a scientist at Yahoo! Labs in Sunnyvale, California. Zhongfei Zhang is an associate professor in the computer science department at the State University of New York in Binghamton. Philip S. Yu is a professor in the computer science department and the Wexler Chair in Information Technology at the University of Illinois in Chicago. Klappentext A culmination of the authors' years of extensive research on the topic, this book provides a complete, self-contained introduction to advances in relational data clustering. It presents a novel theoretical framework as well as the most state-of-the-art models for relational data clustering. The authors also describe a family of new algorithms for various relational clustering problems arising in a wide range of important applications. Concepts and theory are explained through detailed application examples and the book includes a comprehensive reference list of related literature. Zusammenfassung Presents a comprehensive overview of relational data clustering in data mining research. This book reflects the emergence of relational data clustering as an important field of data clustering, with applications in text mining, social network analysis, collaborative filtering, and bioinformatics. Inhaltsverzeichnis Introduction. Models. Algorithms. Applications. Summary. References. Index.

Product details

• Authors: Bo Long, Bo Zhang Long, Long Bo, Philip S. Yu, Yu Philip S., Zhongfei Zhang, Zhang Zhongfei
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Hardback
• Released: 19.05.2010
• EAN: 9781420072617
• ISBN: 978-1-4200-7261-7
• No. of pages: 212
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > Application software; Data Mining, COMPUTERS / General, BUSINESS & ECONOMICS / Statistics, MATHEMATICS / Probability & Statistics / General, COMPUTERS / Programming / Games, COMPUTERS / Machine Theory, computer science, Probability & statistics, Mathematical & statistical software, Automatic control engineering, Probability and statistics, COMPUTERS / Data Science / Data Analytics, Mathematical and statistical software