DocumentCode :
2735540
Title :
Parallel data mining revisited: Better, not faster
Author :
Berthold, Michael R.
Author_Institution :
Dept. of Bioinf. & Inf. Min., Konstanz Univ., Konstanz, Germany
fYear :
2012
fDate :
13-15 June 2012
Firstpage :
21
Lastpage :
21
Abstract :
Summary form only given. In this talk the author will discuss how 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. He will discuss a number of generic ways of tuning data mining algorithms and elaborate on two prominent examples in more detail. A series of examplatory experiments will be used to illustrate the effect such use of parallel resources can have.
Keywords :
data mining; parallel processing; resource allocation; data mining tuning; distributed compute resource; parallel compute resource; parallel data mining; Accuracy; Artificial intelligence; Bioinformatics; Conferences; Data mining; Educational institutions; Focusing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Engineering Systems (INES), 2012 IEEE 16th International Conference on
Conference_Location :
Lisbon
Print_ISBN :
978-1-4673-2694-0
Electronic_ISBN :
978-1-4673-2693-3
Type :
conf
DOI :
10.1109/INES.2012.6249824
Filename :
6249824
Link To Document :
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