Text Mining: Classification, Clustering, and Applications

An extension of data mining, text mining involves the extraction of information and knowledge from unstructured text. This constantly evolving field is increasingly used by major corporations, such as Google, Yahoo, and Microsoft.

Featuring contributions from leading researchers in the field, this book provides a detailed overview of text mining theory, applications, and visualization. The theory section discusses text mining, information retrieval, latent semantic analysis, pagerank, latent Dirichlet allocation, and probabilistic relational models. In the section on text mining applications, the book explores web-based information, system and safety issues, spam filtering, information extraction, link mining, question answering, determining trends, and news reports. The final section of the book covers text visualization and examines how text mining techniques can be used to map information through visualizing databases.

As the number and size of texts grow in the nearly frictionless environment of computers, the need and ability to extract meaning from them has nearly kept pace. Contributors from many institutions and countries, but whose fields are not identified, explain some of the approaches and techniques for finding various meanings in a corpus. Among their topics are detecting bias in media outlets with statistical learning methods, non-negative matrix and tensor factorization for discussion tacking, the constrained partitional clustering of text data, and utility-based information distillation. Annotation ©2009 Book News, Inc., Portland, OR (booknews.com)

The Definitive Resource on Text Mining Theory and Applications from Foremost Researchers in the Field

Giving 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.