Abstract
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.
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Akbar, Z., Ivanova, V.N., Berthold, M.R. (2012). Parallel Data Mining Revisited. Better, Not Faster. In: Hollmén, J., Klawonn, F., Tucker, A. (eds) Advances in Intelligent Data Analysis XI. IDA 2012. Lecture Notes in Computer Science, vol 7619. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34156-4_4
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DOI: https://doi.org/10.1007/978-3-642-34156-4_4
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