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Survey of Text Mining : Clustering, Classification, and Retrieval by Peg Howland, Haesun Park (auth.), Michael W. Berry (eds.) is a nonfiction available to read on EtoBox.

What is Survey of Text Mining : Clustering, Classification, and Retrieval about?

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- top

Who reads Survey of Text Mining : Clustering, Classification, and Retrieval?

Author

Peg Howland, Haesun Park (auth.), Michael W. Berry (eds.)

Publisher

Springer-Verlag New York

Published

2004

Language

EN

ISBN

9781441930576

Category

nonfiction

Subjects

Computer Science, Mathematics, Networking