Learn How To Convert Web Data Into Web Knowledge
This text demonstrates how to extract knowledge by finding
meaningful connections among data spread throughout the Web.
Readers learn methods and algorithms from the fields of information
retrieval, machine learning, and data mining which, when combined,
provide a solid framework for mining the Web. The authors walk
readers through the algorithms with the aid of examples and
exercises.
This text is divided into three parts:
Part One, Web Structure, presents basic concepts and techniques
for extracting information from the Web. Readers learn how to
collect and index Web documents as well as search and rank Web
pages according to their textual content and hyperlink
structure.
Part Two, Web Content Management, offers two approaches,
clustering and classification, for organizing Web content. For both
approaches, the authors set forth specific algorithms that enable
readers to convert Web data into knowledge.
Part Three, Web Usage Mining, demonstrates the application of
data mining methods to uncover meaningful patterns of Internet
usage.
Methods and algorithms are illustrated by simple examples. More
than 100 exercises help readers assess their grasp of the material.
Further, thirty-four hands-on analysis problems ask readers to use
their new data mining expertise to solve real problems, working
with large data sets. All the data sets needed for the examples,
exercises, and analysis problems are available on the companion Web
site.
The extensive use of examples, along with the opportunity to
test and apply data mining skills, makes this text ideal for
graduate and upper-level undergraduates in computer science
andengineering. Web designers and researchers will find that this
text gives them a new set of tools to further mine the Web for
knowledge and move well beyond the capabilities of standard search
engines.
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