Survey of Text Mining: Clustering, Classification, and Retrieval: No. 1

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ISBN 10: 0387955631 / ISBN 13: 9780387955636

Editore: -, 2003

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Survey of Text Mining: Clustering, Classification, and Retrieval: No. 1 This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping.

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Dati bibliografici

• Titolo: Survey of Text Mining: Clustering, Classification, and Retrieval: No. 1
• Editore: -
• Data di pubblicazione: 2003
• Lingua: Inglese
• ISBN 10: 0387955631
• ISBN 13: 9780387955636
• Rilegatura: Hardcover
• Condizione: Very Good

Descrizione articolo

Riassunto

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.

Dalla quarta di copertina

 

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 codified and adapted for changing needs and environments.

Survey of Text Mining is a comprehensive edited survey organized into three parts: Clustering and Classification; Information Extraction and Retrieval; and Trend Detection. Many of the chapters stress the practical application of software and algorithms for current and future needs in text mining. Authors from industry provide their perspectives on current approaches for large-scale text mining and obstacles that will guide R&D activity in this area for the next decade.

Topics and features:

* Highlights issues such as scalability, robustness, and software tools

* Brings together recent research and techniques from academia and industry

* Examines algorithmic advances in discriminant analysis, spectral clustering, trend detection, and synonym extraction

* Includes case studies in mining Web and customer-support logs for hot- topic extraction and query characterizations

* Extensive bibliography of all references, including websites

This useful survey volume taps the expertise of academicians and industry professionals to recommend practical approaches to purifying, indexing, and mining textual information. Researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining, who need the latest text-mining methods and algorithms, will find the book an indispensable resource.