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A Survey of Parallel Data Mining

Freitas, Alex A. (1996) A Survey of Parallel Data Mining. In: 2nd International Conference on the Practical Applications of Knowledge Discovery and Data Mining, 02-04 Aug 1996, Portland, Oregon, USA. (Unpublished) (KAR id:21570)

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Abstract

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons

learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 21 Aug 2009 22:10 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21570 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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