Mining Very Large Databases with Parallel Processing Download

Author: Alex A. Freitas
Edition: 1st
Binding: Hardcover
ISBN: 0792380487

Mining Very Large Databases with Parallel Processing (Advances in Database Systems)

Mining Very Large Databases with Parallel Processing addresses the problem of large-scale data mining. Get Mining Very Large Databases with Parallel Processing computer books for free.
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 co Check Mining Very Large Databases with Parallel Processing our best computer books for 2013. All books are available in pdf format and downloadable from rapidshare, 4shared, and mediafire.

Mining Very Large Databases with Parallel Processing Download

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 t 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 co

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