Swarm optimisation as a new tool for data mining

This paper proposes the use of particle swarm optimisers as a tool for data mining. To evaluate its usefulness, we empirically compare the performance of three variants of the particle optimiser with another evolutionary algorithm, namely a genetic algorithm, in rule discovery for classification tasks. Such tasks are considered core tools for decision support systems in a widespread area, ranging from the industry, commerce, military and scientific fields. The data sources used here for experimental testing are commonly used and considered as a de facto standard for rule discovery algorithms reliability ranking. The results obtained in these domains seem to indicate that particle swarm optimisers are competitive with other evolutionary techniques, and can be successfully applied to more demanding problem domains.