The University of Southampton
University of Southampton Institutional Repository

Editorial survey: swarm intelligence for data mining

Martens, David, Fawcett, Tom and Baesens, Bart (2011) Editorial survey: swarm intelligence for data mining [in special issue: Swarm Intelligence] Machine Learning, 82, (1), pp. 1-42. (doi:10.1007/s10994-010-5216-5).

Record type: Article

Abstract

This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.

Full text not available from this repository.

More information

Published date: January 2011
Keywords: swarm intelligence, ant colony optimization, particle swarm optimization, data mining
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336474
URI: http://eprints.soton.ac.uk/id/eprint/336474
PURE UUID: e7b3a4e3-5169-461b-be29-ac0bc88eacea

Catalogue record

Date deposited: 27 Mar 2012 13:23
Last modified: 18 Jul 2017 06:07

Export record

Altmetrics

Contributors

Author: David Martens
Author: Tom Fawcett
Author: Bart Baesens

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×