000 04891nam a22005655i 4500
001 978-1-84996-226-1
003 DE-He213
005 20140220084516.0
007 cr nn 008mamaa
008 100623s2010 xxk| s |||| 0|eng d
020 _a9781849962261
_9978-1-84996-226-1
024 7 _a10.1007/978-1-84996-226-1
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aWeiss, Sholom M.
_eauthor.
245 1 0 _aFundamentals of Predictive Text Mining
_h[electronic resource] /
_cby Sholom M. Weiss, Nitin Indurkhya, Tong Zhang.
264 1 _aLondon :
_bSpringer London,
_c2010.
300 _aXIV, 283p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTexts in Computer Science,
_x1868-0941 ;
_v41
505 0 _aOverview 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.
520 _aOne consequence of the pervasive use of computers is that most documents originate in digital form. Widespread use of the Internet makes them readily available. Text mining - the process of analyzing unstructured natural-language text – is concerned with how to extract information from these documents. Developed from the authors' highly successful Springer reference on text mining, Fundamentals of Predictive Text Mining is an introductory textbook and guide to this rapidly evolving field. Integrating topics spanning the varied disciplines of data mining, machine learning, databases, and computational linguistics, this uniquely useful book also provides practical advice for text mining. In-depth discussions are presented on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Background on data mining is beneficial, but not essential. Where advanced concepts are discussed that require mathematical maturity for a proper understanding, intuitive explanations are also provided for less advanced readers. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Includes access to industrial-strength text-mining software that runs on any computer. Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students. 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.
650 0 _aComputer science.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aText processing (Computer science.
650 0 _aInformation systems.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aDocument Preparation and Text Processing.
650 2 4 _aComputer Appl. in Administrative Data Processing.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aDatabase Management.
700 1 _aIndurkhya, Nitin.
_eauthor.
700 1 _aZhang, Tong.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781849962254
830 0 _aTexts in Computer Science,
_x1868-0941 ;
_v41
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-84996-226-1
912 _aZDB-2-SCS
999 _c110992
_d110992