Parallel Data Mining Revisited. Better, Not Faster

In this paper we argue that parallel and/or distributed compute resources can be used differently: instead of focusing on speeding up algorithms, we propose to focus on improving accuracy. In a nutshell, the goal is to tune data mining algorithms to produce better results in the same time rather tha...

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Bibliographic Details
Published inAdvances in Intelligent Data Analysis XI pp. 23 - 34
Main Authors Akbar, Zaenal, Ivanova, Violeta N., Berthold, Michael R.
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2012
SeriesLecture Notes in Computer Science
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ISBN9783642341557
3642341551
ISSN0302-9743
1611-3349
DOI10.1007/978-3-642-34156-4_4

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Summary:In this paper we argue that parallel and/or distributed compute resources can be used differently: instead of focusing on speeding up algorithms, we propose to focus on improving accuracy. In a nutshell, the goal is to tune data mining algorithms to produce better results in the same time rather than producing similar results a lot faster. We discuss a number of generic ways of tuning data mining algorithms and elaborate on two prominent examples in more detail. A series of exemplary experiments is used to illustrate the effect such use of parallel resources can have.
ISBN:9783642341557
3642341551
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-34156-4_4