highly successful reference text mining Springer, basics Predictive Text Mining is a textbook introduction and guide to this rapidly growing field. Integrating topics covering various disciplines of data mining, machine learning, databases, and computational linguistics, this book uniquely useful also provides practical suggestions for text mining. in-depth discussion is presented on the issue of classification of documents, information retrieval, grouping and organizing documents, information extraction, web-based data-sourcing, and the prediction and evaluation. Topics and features: introduction presents a comprehensive, practical and easy to read for text mining, including chapter summaries, historical commentary and a useful bibliography, and classroom-tested exercises for each chapter, explores the application and utility of each method, as well as the optimum technique for specific scenarios; provides several descriptive case studies that take the reader from the description of the problem for the dissemination of real-world systems, including access to text-mining software-power industry that runs on any computer; describe a method that relies on basic statistical techniques, allowing for relevance for all languages ​​(not just English), contains links to free downloadable software and additional material other instructions. Fundamentals of Predictive Text Mining is an important resource for IT professionals and managers, as well as the key text for advanced undergraduate computer science students and beginning graduate students. Weiss is a Research Staff Member with IBM's Predictive Modeling group, Yorktown Heights in New York, and Professor Emeritus of Computer Science at Rutgers University. Nitin Indurkhya is a Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, and founder and president of consulting firm data mining Data-Miner Pty Ltd Tong Zhang is Associate Professor in the Department of Statistics and biostatistics at Rutgers University, New Jersey.