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Köp båda 2 för 1033 krFrom the reviews: "This is a practical, up-to-date account of the various techniques for dealing intelligently with free text. It would be an invaluable resource to any advanced undergraduate student interested in information retrieval." (Patrick Oladimeji, Times Higher Education, 26 May 2011) This is a well-written and interesting text for information technology (IT) professionals and computer science students. It seems to address all of the topics related to the fields that, when integrated, are known as knowledge engineering. Without a doubt, the authors experience in the field makes this book a successful contribution to the literature that targets the interests of the IT community and beyond. (Jolanta Mizera-Pietraszko, ACM Computing Reviews, June, 2011) This well-written work, which offers a unifying view of text mining through a systematic introduction to solving real-world problems. The uniqueness of this book is the recourse to the prediction problem, which, by providing practical advice, allows for the integration of related topics. The book is accompanied by a software implementation of the main algorithmic practices introduced. This is the icing on the cake for both beginners and expert readers . This is the book I have always wanted to read. (Ernesto DAvenzo, ACM Computing Reviews, August, 2012)
Dr. Sholom M. Weiss is a Research Staff Member with the IBM Predictive Modeling group, in Yorktown Heights, New York, and Professor Emeritus of Computer Science at Rutgers University. Dr. Nitin Indurkhya is Professor at the School of Computer Science and Engineering, University of New South Wales, Australia, as well as founder and president of data-mining consulting company Data-Miner Pty Ltd. Dr. Tong Zhang is Associate Professor at the Department of Statistics and Biostatistics at Rutgers University, New Jersey.
Overview of Text Mining.- From Textual Information to Numerical Vectors.- Using Text for Prediction.- Information Retrieval and Text Mining.- Finding Structure in a Document Collection.- Looking for Information in Documents.- Data Sources for Prediction: Databases, Hybrid Data and the Web.- Case Studies.- Emerging Directions.