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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kuncheva, L.I. Hadjitodorov, S.T. Todorova, L.P. |
| Copyright Year | 2006 |
| Description | Author affiliation: Sch. of Informatics, Univ. of Wales, Bangor (Kuncheva, L.I.) |
| Abstract | Cluster ensembles are deemed to be a robust and accurate alternative to single clustering runs. 24 methods for designing cluster ensembles are compared here using 24 data sets, both artificial and real. Adjusted rand index and classification accuracy are used as accuracy criteria with respect to a known partition assumed to be the "true" one. The data sets are randomly chosen to represent medium-size problems arising within a variety of biomedical domains. Ensemble size of 10 was considered. It was found that there is a significant difference among the compared methods (Friedman's two way ANOVA). The best ensembles were based on k-means individual clusterers. Consensus functions interpreting the consensus matrix of the ensemble as data, rather than similarity, were found to be significantly better than the traditional alternatives, including CSPA and HGPA |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 8376408 |
| Page Count | 7 |
| File Format | |
| ISBN | 1424409535 |
| DOI | 10.1109/ICIF.2006.301614 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-07-10 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | ISIF |
| Subject Keyword | Design methodology Clustering algorithms Clustering methods Couplings Informatics Robustness Analysis of variance Bioinformatics Laboratories Biomedical engineering Biomedical data Clustering Cluster ensembles Consensus functions Experimental comparison |
| Content Type | Text |
| Resource Type | Article |
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