Survey of Text Mining II
Clustering, Classification, and Retrieval
(Sprache: Englisch)
This Second Edition explores the field of text mining. Coverage includes the use of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval.
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This Second Edition explores the field of text mining. Coverage includes the use of machine learning in conjunction with natural language processing, information extraction, and algebraic/mathematical approaches to computational information retrieval.
Klappentext zu „Survey of Text Mining II “
This Second Edition brings readers thoroughly up to date with the emerging 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. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. Authors highlight open research questions in document categorization, clustering, and trend detection. In addition, the book describes new application problems in areas such as email surveillance and anomaly detection.
Inhaltsverzeichnis zu „Survey of Text Mining II “
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.
Autoren-Porträt
The development of techniques for mining unstructured, semi-structured, and fully structured textual data has become critical in both academia and industry. This Second Edition brings readers thoroughly up to date with the emerging 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. The book explores a broad range of issues, ranging from the development of new learning approaches to the parallelization of existing algorithms. In addition, it describes new application problems in areas such as email surveillance and anomaly detection. Presenting a comprehensive selection of topics within the field, this book is an indispensable resource for researchers, practitioners, and professionals involved in information retrieval, computational statistics, and datamining.
Bibliographische Angaben
- 2010, Softcover reprint of hardcover 1st ed. 2008, XVI, 240 Seiten, Masse: 15,2 x 22,9 cm, Kartoniert (TB), Englisch
- Herausgegeben: Michael W. Berry, Malu Castellanos
- Verlag: Springer, Berlin
- ISBN-10: 184996713X
- ISBN-13: 9781849967136
Sprache:
Englisch
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