A Survey of Parallel Data Mining

Freitas, A.A. (1998) A Survey of Parallel Data Mining. In: Proc 2nd Int Conf on the Practical Applications of Knowledge Discovery and Data Mining.

Postscript
Download (730Kb)	Preview
PDF
Download (143Kb)	Preview

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:	Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User:	Mark Wheadon
Date Deposited:	21 Aug 2009 22:10
Last Modified:	08 Jun 2012 13:50
Resource URI:	http://kar.kent.ac.uk/id/eprint/21570 (The current URI for this page, for reference purposes)

• Depositors only (login required):