http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Clustering, Classification, and Retrieval
The proliferation of digital computing devices and their use in communication has resulted in an increased demand for systems and algorithms capable of mining textual data. Thus, the development of techniques for mining unstructured, semi-structured, and fully-structured textual data has become increasingly important in both academia and industry. This second volume continues to survey the evolving field of text mining - the application of techniques of machine learning, in conjunction with natural language processing, information extraction and algebraic/mathematical approaches, to computational information retrieval. Numerous diverse issues are addressed, ranging from the development of new learning approaches to novel document clustering algorithms, collectively spanning several major topic areas in text mining. Features: • Acts as an important benchmark in the development of current and future approaches to mining textual information • Serves as an excellent companion text for courses in text and data mining, information retrieval and computational statistics • Experts from academia and industry share their experiences in solving large-scale retrieval and classification problems • Presents an overview of current methods and software for text mining • Highlights open research questions in document categorization and clustering, and trend detection • Describes new application problems in areas such as email surveillance and anomaly detection Survey of Text Mining II offers a broad selection in state-of-the art algorithms and software for text mining from both academic and industrial perspectives, to generate interest and insight into the state of the field. This book will be an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and data mining. Michael W. Berry is a professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. Malu Castellanos is a senior researcher at Hewlett-Packard Laboratories in Palo Alto, California.
Clustering -- Cluster-Preserving Dimension Reduction Methods for Document Classification -- Automatic Discovery of SimilarWords -- Principal Direction Divisive Partitioning with Kernels and k-Means Steering -- Hybrid Clustering with Divergences -- Text Clustering with Local Semantic Kernels -- Document Retrieval and Representation -- Vector Space Models for Search and Cluster Mining -- Applications of Semidefinite Programming in XML Document Classification -- Email Surveillance and Filtering -- Discussion Tracking in Enron Email Using PARAFAC -- Spam Filtering Based on Latent Semantic Indexing -- Anomaly Detection -- A Probabilistic Model for Fast and Confident Categorization of Textual Documents -- Anomaly Detection Using Nonnegative Matrix Factorization -- Document Representation and Quality of Text: An Analysis.
edited by Michael W. Berry, Malu Castellanos.
Data Structures, Cryptology and Information Theory
Document Preparation and Text Processing
Information Systems Applications (incl. Internet)