The Resource Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Resource Information
The item Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
- Summary
- One 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
- Language
- eng
- Extent
- 1 online resource (xiii, 226 pages).
- Contents
-
- 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
- Isbn
- 9781849962261
- Label
- Fundamentals of predictive text mining
- Title
- Fundamentals of predictive text mining
- Statement of responsibility
- Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
- Language
- eng
- Summary
- One 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
- Cataloging source
- GW5XE
- http://library.link/vocab/creatorName
- Weiss, Sholom M
- Dewey number
- 006.3/12
- Index
- index present
- Language note
- English
- LC call number
- QA76.9.D343
- LC item number
- W45 2010
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorDate
- 1971-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Indurkhya, Nitin
- Zhang, Tong
- Series statement
- Texts in computer science
- http://library.link/vocab/subjectName
-
- Data mining
- Informatique
- Data mining
- Label
- Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
- Bibliography note
- Includes bibliographical references (pages 211-217) and indexes
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 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
- Control code
- 654397211
- Dimensions
- unknown
- Extent
- 1 online resource (xiii, 226 pages).
- Form of item
- online
- Isbn
- 9781849962261
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-84996-226-1
- http://library.link/vocab/ext/overdrive/overdriveId
- 978-1-84996-225-4
- Specific material designation
- remote
- System control number
- (OCoLC)654397211
- Label
- Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
- Bibliography note
- Includes bibliographical references (pages 211-217) and indexes
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Color
- multicolored
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
- 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
- Control code
- 654397211
- Dimensions
- unknown
- Extent
- 1 online resource (xiii, 226 pages).
- Form of item
- online
- Isbn
- 9781849962261
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-1-84996-226-1
- http://library.link/vocab/ext/overdrive/overdriveId
- 978-1-84996-225-4
- Specific material designation
- remote
- System control number
- (OCoLC)654397211
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.missouri.edu/portal/Fundamentals-of-predictive-text-mining-Sholom-M./i-HD5S25fgA/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.missouri.edu/portal/Fundamentals-of-predictive-text-mining-Sholom-M./i-HD5S25fgA/">Fundamentals of predictive text mining, Sholom M. Weiss, Nitin Indurkhya, Tong Zhang</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.missouri.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.missouri.edu/">University of Missouri Libraries</a></span></span></span></span></div>
