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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2013

01.04.2013 | Regular Paper

Finding best algorithmic components for clustering microarray data

verfasst von: Milan Vukićević, Kathrin Kirchner, Boris Delibašić, Miloš Jovanović, Johannes Ruhland, Milija Suknović

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2013

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Abstract

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.
Vorheriger Artikel Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining
Nächster Artikel On measuring the performance of binary classifiers

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Metadaten
Titel
Finding best algorithmic components for clustering microarray data
verfasst von
Milan Vukićević
Kathrin Kirchner
Boris Delibašić
Miloš Jovanović
Johannes Ruhland
Milija Suknović
Publikationsdatum
01.04.2013
Verlag
Springer-Verlag
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2013
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-012-0542-5

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