dc.contributor.author | Moggridge, Paul | |
dc.contributor.author | Helian, Na | |
dc.contributor.author | Sun, Yi | |
dc.contributor.author | Lilley, Mariana | |
dc.date.accessioned | 2023-10-24T11:30:03Z | |
dc.date.available | 2023-10-24T11:30:03Z | |
dc.date.issued | 2023-10-04 | |
dc.identifier.citation | Moggridge , P , Helian , N , Sun , Y & Lilley , M 2023 , ' On Instance Weighted Clustering Ensembles ' , Paper presented at The 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , Bruges , Belgium , 4/10/23 - 6/10/23 . | |
dc.identifier.citation | conference | |
dc.identifier.other | ORCID: /0000-0003-1004-1298/work/145463508 | |
dc.identifier.other | ORCID: /0000-0001-6687-0306/work/145463601 | |
dc.identifier.uri | http://hdl.handle.net/2299/26973 | |
dc.description | © ESANN, 2023. This is the accepted manuscript version of an article which has been published in final form at: www.esann.org/proceedings/2023 | |
dc.description.abstract | Ensemble clustering is a technique which combines multipleclustering results, and instance weighting is a technique which highlightsimportant instances in a dataset. Both techniques are known to enhanceclustering performance and robustness. In this research, ensembles andinstance weighting are integrated with the spectral clustering algorithm.We believe this is the first attempt at creating diversity in the generativemechanism using density based instance weighting for a spectral ensemble.The proposed approach is empirically validated using synthetic datasetscomparing against spectral and a spectral ensemble with random instanceweighting. Results show that using the instance weighted sub-samplingapproach as the generative mechanism for an ensemble of spectral cluster-ing leads to improved clustering performance on datasets with imbalancedclusters. | en |
dc.format.extent | 6 | |
dc.format.extent | 566207 | |
dc.language.iso | eng | |
dc.title | On Instance Weighted Clustering Ensembles | en |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.contributor.institution | Biocomputation Research Group | |
dc.contributor.institution | Department of Computer Science | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.description.status | Peer reviewed | |
dc.date.embargoedUntil | 2023-10-06 | |
rioxxterms.type | Other | |
herts.preservation.rarelyaccessed | true | |