http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Clustering, Classification, and Retrieval
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.
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 -- Index.
Information storage and retrieval systems