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), 1-42. (doi:10.1007/s10994-010-5216-5).

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Description/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.

Item Type: Article
ISSNs: 0885-6125 (print)
1573-0565 (electronic)
Keywords: swarm intelligence, ant colony optimization, particle swarm optimization, data mining
Subjects: Q Science > QA Mathematics > QA76 Computer software
Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Business and Law > Southampton Management School
ePrint ID: 336474
Date Deposited: 27 Mar 2012 13:23
Last Modified: 27 Mar 2014 20:20
URI: http://eprints.soton.ac.uk/id/eprint/336474

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