On Instance Weighted Clustering Ensembles

Paul Moggridge, Na Helian, Yi Sun, Mariana Lilley

Research output: Contribution to conferencePaperpeer-review

12 Downloads (Pure)

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.
Original languageEnglish
Number of pages6
Publication statusPublished - 4 Oct 2023
EventThe 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 4 Oct 20236 Oct 2023
https://www.esann.org/

Conference

ConferenceThe 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Country/TerritoryBelgium
CityBruges
Period4/10/236/10/23
Internet address

Fingerprint

Dive into the research topics of 'On Instance Weighted Clustering Ensembles'. Together they form a unique fingerprint.

Cite this