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Survey of Text Mining : Clustering, Classification, and Retrieval /

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As the volume of digitized textual information continues to grow, so does the critical need for designing robust and scalable indexing and search strategies/software to meet a variety of user needs. Knowledge extraction or creation from text requires systematic, yet reliable processing that can be c...

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Bibliographic Details
Main Author: Berry, Michael W.
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: New York, NY : Springer New York, 2004.
Subjects:
Computer science.
Information systems.
Information storage and retrieval systems.
Multimedia systems.
Mathematics.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Clustering & Classification: Cluster-preserving dimension reduction methods for efficient classification of text data
  • Automatic discovery of similar words
  • Simultaneous clustering and dynamic keyword weighting for text documents
  • Feature selection and document clustering; II: Information Extraction & Retrieval: Vector space models for search and cluster mining
  • HotMiner?Discovering hot topics from dirty text
  • Combining families of information retrieval algorithms using meta-learning; III: Trend Detection: Trend and behavior detection from Web queries
  • A survey of emerging trend detection in textual data mining
  • Index.