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.
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