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Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > Parallel processing
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Mining Very Large Databases with Parallel Processing (Hardcover, 2000 ed.)
Loot Price: R6,000
Discovery Miles 60 000
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Mining Very Large Databases with Parallel Processing (Hardcover, 2000 ed.)
Series: Advances in Database Systems, 9
Expected to ship within 10 - 15 working days
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Mining Very Large Databases with Parallel Processing addresses the
problem of large-scale data mining. It is an interdisciplinary
text, describing advances in the integration of three computer
science areas, namely intelligent' (machine learning-based) data
mining techniques, relational databases and parallel processing.
The basic idea is to use concepts and techniques of the latter two
areas - particularly parallel processing - to speed up and scale up
data mining algorithms. The book is divided into three parts. The
first part presents a comprehensive review of intelligent data
mining techniques such as rule induction, instance-based learning,
neural networks and genetic algorithms. Likewise, the second part
presents a comprehensive review of parallel processing and parallel
databases. Each of these parts includes an overview of
commercially-available, state-of-the-art tools. The third part
deals with the application of parallel processing to data mining.
The emphasis is on finding generic, cost-effective solutions for
realistic data volumes. Two parallel computational environments are
discussed, the first excluding the use of commercial-strength DBMS,
and the second using parallel DBMS servers. It is assumed that the
reader has a knowledge roughly equivalent to a first degree (BSc)
in accurate sciences, so that (s)he is reasonably familiar with
basic concepts of statistics and computer science. The primary
audience for Mining Very Large Databases with Parallel Processing
is industry data miners and practitioners in general, who would
like to apply intelligent data mining techniques to large amounts
of data. The book will also be of interest to academic researchers
and postgraduate students, particularly database researchers,
interested in advanced, intelligent database applications, and
artificial intelligence researchers interested in industrial,
real-world applications of machine learning.
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