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Parallel rule induction with information theoretic pre-pruning

Stahl, F., Bramer, M. and Adda, M. (2010) Parallel rule induction with information theoretic pre-pruning. In: Research and Development in Intelligent Systems XXVI. Springer, London, pp. 151-164. ISBN 9781848829824

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To link to this item DOI: 10.1007/978-1-84882-983-1_11

Abstract/Summary

In a world where data is captured on a large scale the major challenge for data mining algorithms is to be able to scale up to large datasets. There are two main approaches to inducing classification rules, one is the divide and conquer approach, also known as the top down induction of decision trees; the other approach is called the separate and conquer approach. A considerable amount of work has been done on scaling up the divide and conquer approach. However, very little work has been conducted on scaling up the separate and conquer approach.In this work we describe a parallel framework that allows the parallelisation of a certain family of separate and conquer algorithms, the Prism family. Parallelisation helps the Prism family of algorithms to harvest additional computer resources in a network of computers in order to make the induction of classification rules scale better on large datasets. Our framework also incorporates a pre-pruning facility for parallel Prism algorithms.

Item Type:Book or Report Section
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:30150
Publisher:Springer

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