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Scaling up classification rule induction through parallel processing

Stahl, F. ORCID: and Bramer, M. (2012) Scaling up classification rule induction through parallel processing. Knowledge Engineering Review. pp. 243-259. ISSN 1469-8005

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To link to this item DOI: 10.1017/S0269888912000355


The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

Item Type:Article
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30159
Publisher:Cambridge University Press


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