Show simple item record

dc.contributor.authorFahy, Conoren
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorGongora, Mario Augustoen
dc.date.accessioned2017-03-27T10:15:24Z
dc.date.available2017-03-27T10:15:24Z
dc.date.issued2017-06
dc.identifier.citationFahy, C., Yang, S. and Gongora, M. (2017) Finding multi-density clusters in non-stationary data streams using an ant colony with adaptive parameters. Proceedings of the 2017 IEEE Congress on Evolutionary Computation, in pressen
dc.identifier.urihttp://hdl.handle.net/2086/13855
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractDensity based methods have been shown to be an effective approach for clustering non-stationary data streams. The number of clusters does not need to be known a priori and density methods are robust to noise and changes in the statistical properties of the data. However, most density approaches require sensitive, data dependent parameters. These parameters greatly affect the clustering performance and in a dynamic stream a good set of parameters at time t are not necessarily the best at time t+1. Furthermore, these parameters are global and so restrict the algorithm to finding clusters of the same density. In this paper, we propose a density based algorithm with adaptive parameters which are local to each discovered cluster. The algorithm, denoted Ant Colony Multi-Density Clustering (ACMDC), uses artificial ants to form nests in dense areas of the data. As the ants move between nests, their collective memory is stored in the form of pheromone trails. Clusters are identified as groups of similar nests. The proposed algorithm is evaluated across a number of synthetic data streams containing overlapping and embedded multi-density clusters. The performance of the algorithm is shown to be favourable to a leading density based stream-clustering algorithm despite requiring no tunable parameters.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.titleFinding multi-density clusters in non-stationary data streams using an ant colony with adaptive parametersen
dc.typeConferenceen
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-03-07en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record