Distributed Data Mining

author: Giuseppe di Fatta, University of Konstanz
published: Feb. 25, 2007,   recorded: July 2005,   views: 10820

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Data mining is the automated analysis of large volumes of data looking for relationships and knowledge that are implicit in data. Data mining and knowledge discovery in large amounts of data can benefit from the use of parallel and distributed computational environments to improve both performance and quality of data selection. The goal of this tutorial is to provide researchers and practitioners with an introduction to mining large data sets by exploiting techniques from high performance parallel and distributed computing.

This tutorial is organized in two parts. In the first part an introduction to high performance parallel and distributed computing is provided. Different forms of parallelism that can be exploited in data mining techniques and algorithms are analyzed. The second part presents a review of distributed data mining approaches. For each data mining technique, different ways for parallel implementation are presented and discussed. Furthermore, parallel and distributed data mining systems and algorithms are discussed. Finally, current research issues and perspectives in high-performance data mining are outlined.

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Reviews and comments:

Comment1 Hooman, November 9, 2007 at 12:31 a.m.:

I want to see the second part of this talk as well.

Comment2 Lolita, March 29, 2009 at 9:33 p.m.:

Very Interesting speech thank you.
Can you please post the second part as well.

Comment3 Sean, October 21, 2009 at 12:04 a.m.:

I like this speech. Can you post the second part of it?
Thank you.

Comment4 Suan, December 5, 2009 at 7:43 a.m.:

How can I see the second part of it?

Comment5 Faisal, January 24, 2010 at 5:48 p.m.:

where is the second part please?

Comment6 abbas alizadeh, November 30, 2011 at 5:24 p.m.:

Hi. very useful lecture, but who and where I can get the second part of this lecture?

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