Overview
- Presents a comprehensive, practical and easy-to-read introduction to text mining
- Updated and expanded with new content on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation
- Includes chapter summaries, classroom-tested exercises, and several descriptive case studies
- Includes supplementary material: sn.pub/extras
- Request lecturer material: sn.pub/lecturer-material
Part of the book series: Texts in Computer Science (TCS)
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Table of contents (9 chapters)
Reviews
“Fundamentals of predictive text mining is a second edition that is designed as a textbook, with questions and exercises in each chapter. … The book can be used with data mining software for hands-on experience for students. … The book will be very useful for people planning to go into this field or to learn techniques that could be used in a big data environment.” (S. Srinivasan, Computing Reviews, February, 2016)
Authors and Affiliations
About the authors
Dr. Sholom M. Weiss is a Professor Emeritus of Computer Science at Rutgers University, a Fellow of the Association for the Advancement of Artificial Intelligence, and co-founder of AI Data-Miner LLC, New York.
Dr. Nitin Indurkhya is faculty member at the School of Computer Science and Engineering, University of New South Wales, Australia, and the Institute of Statistical Education, Arlington, VA, USA. He is also a co-founder of AI Data-Miner LLC, New York.
Dr. Tong Zhang is a Professor of Statistics and Biostatistics at Rutgers University.
Bibliographic Information
Book Title: Fundamentals of Predictive Text Mining
Authors: Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Series Title: Texts in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-6750-1
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2015
Hardcover ISBN: 978-1-4471-6749-5Published: 14 September 2015
Softcover ISBN: 978-1-4471-7113-3Published: 29 October 2016
eBook ISBN: 978-1-4471-6750-1Published: 07 September 2015
Series ISSN: 1868-0941
Series E-ISSN: 1868-095X
Edition Number: 2
Number of Pages: XIII, 239
Number of Illustrations: 115 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Natural Language Processing (NLP), Computer Appl. in Administrative Data Processing, Information Storage and Retrieval, Database Management