Handbook of research on text and Web mining techologies

Min Song, Min Song, Yi-Fang Brook Wu9781599049908, 1599049902, 9781599049915, 1599049910

The massive daily overflow of electronic data to information seekers creates the need for better ways to digest and organize this information to make it understandable and useful. Text mining, a variation of data mining, extracts desired information from large, unstructured text collections stored in electronic forms.
The Handbook of Research on Text and Web Mining Technologies is the first comprehensive reference to the state of research in the field of text mining, serving a pivotal role in educating practitioners in the field. This compendium of pioneering studies from leading experts is essential to academic reference collections and introduces researchers and students to cutting-edge techniques for gaining knowledge discovery from unstructured text.

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Table of contents :
Editorial Advisory Board……Page 4
List of Contributors……Page 5
Table of Contents……Page 8
Detailed Table of Contents……Page 16
Foreword……Page 34
Preface……Page 35
About the Editors……Page 37
On Document Representation
and Term Weights in
Text Classification……Page 40
Deriving Document Keyphrases
for Text Mining……Page 62
Intelligent Text Mining:
Putting Evolutionary Methods and
Language Technologies Together……Page 76
Automatic Syllabus Classification
Using Support Vector Machines……Page 100
Partially Supervised Text
Categorization……Page 114
Image Classification and
Retrieval with Mining
Technologies……Page 135
Improving Techniques for Naïve
Bayes Text Classifiers……Page 150
Using the Text Categorization
Framework for Protein
Classification……Page 167
Featureless Data Clustering……Page 180
Swarm Intelligence in Text
Document Clustering……Page 204
Some Efficient and Fast
Approaches to Document
Clustering……Page 220
SOM-Based Clustering of
Textual Documents Using
WordNet……Page 228
A Multi-Agent Neural Network
System for Web Text Mining……Page 240
Frequent Mining on XML
Documents……Page 266
The Process and Application of
XML Data Mining……Page 288
Approximate Range Querying
over Sliding Windows……Page 312
Slicing and Dicing a Linguistic
Data Cube……Page 327
Discovering Personalized Novel
Knowledge from Text……Page 340
Untangling BioOntologies for
Mining Biomedical Information……Page 353
Thesaurus-Based
Automatic Indexing……Page 370
Concept-Based Text Mining……Page 385
Statistical Methods for User
Profiling in Web Usage Mining……Page 398
Web Mining to Identify People
of Similar Background……Page 408
Hyperlink Structure Inspired by
Web Usage……Page 425
Designing and Mining
Web Applications:
A Conceptual Modeling Approach……Page 440
Web Usage Mining for
Ontology Management……Page 457
A Lattice-Based Framework for
Interactively and Incrementally
Mining Web Traversal Patterns……Page 487
Privacy-Preserving Data Mining
on the Web:
Foundations and Techniques……Page 507
Automatic Reference Tracking……Page 522
Determination of Unithood and
Termhood for Term Recognition……Page 539
Retrieving Non-Latin
Information in a Latin Web:
The Case of Greek……Page 569
Latent Semantic Analysis and
Beyond……Page 585
Question Answering Using Word
Associations……Page 610
The Scent of a Newsgroup:
Providing Personalized Access to Usenet
Sites through Web Mining……Page 643
Text Mining in Program Code……Page 665
A Study of Friendship Networks
and Blogosphere……Page 685
An HL7-Aware Decision
Support System for E-Health……Page 709
Multitarget Classifiers for
Mining in Bioinformatics……Page 723
Current Issues and Future
Analysis in Text Mining for
Information Security
Applications……Page 733
Collaborative Filtering Based
Recommendation Systems……Page 747
Performance Evaluation
Measures for Text Mining……Page 763
Text Mining in Bioinformatics:
Research and Application……Page 787
Literature Review in
Computational Linguistics
Issues in the Developing Field
of Consumer Informatics:
Finding the Right Information for
Consumer’s Health Information Need……Page 797
A Survey of Selected Software
Technologies for Text Mining……Page 805
Application of Text Mining
Methodologies to Health
Insurance Schedules……Page 824
Web Mining System
for Mobile-Phone Marketing……Page 846
Web Service Architectures
for Text Mining:
An Exploration of the Issues via
an E-Science Demonstrator……Page 861
About the Contributors……Page 880
Index……Page 896