000 04096nam a22005895i 4500
001 978-1-4471-6750-1
003 DE-He213
005 20200421112549.0
007 cr nn 008mamaa
008 150907s2015 xxk| s |||| 0|eng d
020 _a9781447167501
_9978-1-4471-6750-1
024 7 _a10.1007/978-1-4471-6750-1
_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.
250 _a2nd ed. 2015.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _aXIII, 239 p. 115 illus.
_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
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 _aThis successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. 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 Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining 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.
650 0 _aComputer science.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aInformation storage and retrieval.
650 0 _aText processing (Computer science).
650 0 _aApplication software.
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:
_z9781447167495
830 0 _aTexts in Computer Science,
_x1868-0941
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-6750-1
912 _aZDB-2-SCS
942 _cEBK
999 _c58743
_d58743