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 (Unpublished) (KAR id:21570)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) |
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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 Nov 2021 09:59 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/21570 (The current URI for this page, for reference purposes) |
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