A clustering method based on boosting
dc.contributor.author | Frossyniotis, D. | en |
dc.contributor.author | Likas, A. | en |
dc.contributor.author | Stafylopatis, A. | en |
dc.date.accessioned | 2015-11-24T17:00:38Z | |
dc.date.available | 2015-11-24T17:00:38Z | |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10795 | |
dc.rights | Default Licence | - |
dc.subject | ensemble clustering | en |
dc.subject | unsupervised learning | en |
dc.subject | partitions schemes | en |
dc.subject | em algorithm | en |
dc.title | A clustering method based on boosting | en |
heal.abstract | It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. (C) 2004 Elsevier B.V. All rights reserved. | en |
heal.access | campus | - |
heal.fullTextAvailability | TRUE | - |
heal.identifier.primary | DOI 10.1016/j.patrec.2003.12.018 | - |
heal.journalName | Pattern Recognition Letters | en |
heal.journalType | peer reviewed | - |
heal.language | en | - |
heal.publicationDate | 2004 | - |
heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
heal.type | journalArticle | - |
heal.type.el | Άρθρο Περιοδικού | el |
heal.type.en | Journal article | en |
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