Text Mining with MATLAB®

AvRafael E. Banchs

Inbunden, Engelska, 2012

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Beskrivning

Text Mining with MATLAB provides a comprehensive introduction to text mining using MATLAB. It’s designed to help text mining practitioners, as well as those with little-to-no experience with text mining in general, familiarize themselves with MATLAB and its complex applications.

Produktinformation

Utgivningsdatum:2012-08-14
Mått:155 x 235 x 25 mm
Vikt:660 g
Format:Inbunden
Språk:Engelska
Antal sidor:356
Upplaga:2013
Förlag:Springer-Verlag New York Inc.
ISBN:9781461441502

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Recensioner i media

From the reviews: "This book is ideal for professionals and students who require a basic understanding of text mining ... . Starting from the point of zero-knowledge, this book presents fundamental knowledge of text mining with an easy-to-read and mathematically rigorous introduction based on statistical and geometrical models. In addition, the book summarizes the current state of research in this area with some potential applications, including document categorization, searching and content analysis. ... The book is highly recommended for text mining practitioners from basic to intermediate level." (Korhan Gunel, Zentralblatt MATH, Vol. 1254, 2013) "The book offers a nice introduction to techniques and methods for text mining with MATLAB. It is recommended for MATLAB users, especially those who are familiar with writing code. ... beginners will find it interesting and easy to follow. Readers interested in text mining will also find the overall matrix philosophy of MATLAB interesting. Teachers especially will find the book useful for project assignments." (Lefteris Angelis, ACM Computing Reviews, February, 2013)

Innehållsförteckning

Introduction.- Handling Textual Data.- Regular Expressions.- Basic Operations with Strings.- Reading and Writing Files.- Basic Corpus Statistics.- Statistical Models.- Geometrical Models.- Dimensionality Reduction.- Document Categorization.- Document Search.- Content Analysis.