Text Mining (eBook, PDF)

Classification, Clustering, and Applications
Redaktion: Srivastava, Ashok N.; Sahami, Mehran

• Format: PDF

Produktbeschreibung

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the FieldGiving a broad perspective of the field from numerous vantage points, Text Mining: Classification, Clustering, and Applications focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with chapters on the classification of documents into predefined categories. It presents state-of-the-art algorithms and their use in practice. The next chapters describe novel methods for clustering documents into groups that are not predefined. These methods seek to automatically determine topical structures that may exist in a document corpus. The book concludes by discussing various text mining applications that have significant implications for future research and industrial use.There is no doubt that text mining will continue to play a critical role in the development of future information systems and advances in research will be instrumental to their success. This book captures the technical depth and immense practical potential of text mining, guiding readers to a sound appreciation of this burgeoning field.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 328
• Erscheinungstermin: 15. Juni 2009
• Englisch
• ISBN-13: 9781420059458
• Artikelnr.: 42765991

Autorenporträt

Ashok N. Srivastava is the Principal Investigator of the Integrated Vehicle Health Management research project in the NASA Aeronautics Research Mission Directorate. Dr. Srivastava also leads the Intelligent Data Understanding group at NASA Ames Research Center. Mehran Sahami is an Associate Professor and Associate Chair for Education in the computer science department at Stanford University.

Inhaltsangabe

Analysis of Text Patterns Using Kernel Methods. Detection of Bias in Media
Outlets with Statistical Learning Methods. Collective Classification for
Text Classification. Topic Models. Nonnegative Matrix and Tensor
Factorization for Discussion Tracking. Text Clustering with Mixture of von
Mises-Fisher Distributions. Constrained Partitional Clustering of Text
Data: An Overview. Adaptive Information Filtering. Utility-Based
Information Distillation. Text Search Enhanced with Types and Entities.
Index.