Survey of Text Mining : Clustering, Classification, and Retrieval
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Book Description
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.
Book Info
Text provides a survey of text mining, organized into three parts: clustering and classification; information extraction and retrieval; and trend detection. For researchers and practitioners. DLC: Data mining--Congresses.
Survey of Text Mining : Clustering, Classification, and Retrieval,Michael W. Berry,Springer,0387955631,Applied,Artificial Intelligence - General,Cluster analysis,Computer Books: General,Computers,Computers - General Information,Congresses,Data mining,Database Management - General,Discriminant analysis,General,Computers / Information Technology
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