Survey of Text Mining: Clustering, Classification, and Retrieval Softcover reprint of the original 1st ed. 2004 edition

Jacket Description/Back: 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. Description for Sales People: 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. This survey volume draws upon experts in both academia and industry to recommend practical approaches to the purification, indexing, and mining of textual information. Reseachers, practitioners, and professionals in information retrieval who need to know the latest text-mining methods and algorithms will find the book an essential resource. Table of Contents: I: 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 * IndexMarc Notes: Selected conference papers.; Originally published: 2004.; Experts in both academia and industry recommend practical approaches to the purification, indexing, and mining of textual information. They address document identification, clustering and categorizing documents, cleaning text and visualizing semantic models of text.