Mining Very Large Databases with Parallel Processing

Nonfiction, Computers, Advanced Computing, Theory, General Computing
Cover of the book Mining Very Large Databases with Parallel Processing by Simon H. Lavington, Alex A. Freitas, Springer US
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Simon H. Lavington, Alex A. Freitas ISBN: 9781461555216
Publisher: Springer US Publication: December 6, 2012
Imprint: Springer Language: English
Author: Simon H. Lavington, Alex A. Freitas
ISBN: 9781461555216
Publisher: Springer US
Publication: December 6, 2012
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer US

Cover of the book Internet Protocols by Simon H. Lavington, Alex A. Freitas
Cover of the book Technological Systems in the Bio Industries by Simon H. Lavington, Alex A. Freitas
Cover of the book Electrodermal Activity by Simon H. Lavington, Alex A. Freitas
Cover of the book Privacy and the Commercial Use of Personal Information by Simon H. Lavington, Alex A. Freitas
Cover of the book Applied and Computational Control, Signals, and Circuits by Simon H. Lavington, Alex A. Freitas
Cover of the book Parallel Robotic Machine Tools by Simon H. Lavington, Alex A. Freitas
Cover of the book The Psychobiology of Consciousness by Simon H. Lavington, Alex A. Freitas
Cover of the book Learning and Cognition in Autism by Simon H. Lavington, Alex A. Freitas
Cover of the book Agricultural Markets Beyond Liberalization by Simon H. Lavington, Alex A. Freitas
Cover of the book Behavioral Teratogenesis and Behavioral Mutagenesis by Simon H. Lavington, Alex A. Freitas
Cover of the book Life Span Perspectives of Suicide by Simon H. Lavington, Alex A. Freitas
Cover of the book Curbing Population Growth by Simon H. Lavington, Alex A. Freitas
Cover of the book Management Education in the Network Economy by Simon H. Lavington, Alex A. Freitas
Cover of the book Commuting Stress by Simon H. Lavington, Alex A. Freitas
Cover of the book Osteoporosis: Genetics, Prevention and Treatment by Simon H. Lavington, Alex A. Freitas
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy