Document (#31930)

Editor

Berry, M.W.

Title

Survey of text mining : clustering, classification, and retrieval

Imprint

New York, NY : Springer

Year

2004

Pages

XVII, 244 S

Isbn

0-387-95563-1

Abstract

Extracting content from text continues to be an important research problem for information processing and management. Approaches to capture the semantics of text-based document collections may be based on Bayesian models, probability theory, vector space models, statistical models, or even graph theory. As the volume of digitized textual media continues to grow, so does the need for designing robust, 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 codified and adapted for changing needs and environments. This book will draw upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. It will address document identification, clustering and categorizing documents, cleaning text, and visualizing semantic models of text.

Theme

Data Mining

LCSH

Data mining ; Information retrieval
Data mining / Congresses (GBV)
Cluster analysis / Congresses (GBV)
Discriminant analysis / Congresses (GBV)

RSWK

Text Mining / Aufsatzsammlung

BK

54.72 / Künstliche Intelligenz
06.74 / Informationssysteme

DDC

005.741
006.3

LCC

QA76.9.D343

RVK

ST 270 Informatik / Monographien / Software und -entwicklung / Datenbanken, Datenbanksysteme, Data base management, Informationssysteme
ST 302 Informatik / Monographien / Künstliche Intelligenz / Expertensysteme; Wissensbasierte Systeme

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