Parallelism in Knowledge Discovery Techniques

Abstract

Knowledge discovery in databases or data mining is the semi-automated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ’interesting’ in the sense of impacting an organization’s practice. Data mining and knowledge discovery on large amounts of data can benefit of the use of parallel computers both to improve performance and quality of data selection. This paper presents and discusses different forms of parallelism that can be exploited in data mining techniques and algorithms. For the main data mining techniques, such as rule induction, clustering algorithms, decision trees, genetic algorithms, and neural networks, the possible ways to exploit parallelism are presented and discussed in detail. Finally, some promising research directions in the parallel data mining research area are outlined.KeywordsData MiningAssociation RuleCommunication OverheadParallel ImplementationData Mining TechniqueThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.