A Multi-objective Sequential Ensemble for Cluster Structure Analysis and Visualization and Application to Gene Expression View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Noha A. Yousri

ABSTRACT

In the presence of huge high dimensional datasets, it is important to investigate and visualize the connectivity of patterns in huge arbitrary shaped clusters. While density or distance-relatedness based clustering algorithms are used to efficiently discover clusters of arbitrary shapes and densities, classical (yet less efficient) clustering algorithms can be used to analyze the internal cluster structure and visualize it. In this work, a sequential ensemble, that uses an efficient distance-relatedness based clustering, “Mitosis”, followed by the centre-based K-means algorithm, is proposed. K-means is used to segment the clusters obtained by Mitosis into a number of subclusters. The ensemble is used to reveal the gradual change of patterns when applied to gene expression sets. More... »

PAGES

274-283

Book

TITLE

Multiple Classifier Systems

ISBN

978-3-642-12126-5
978-3-642-12127-2

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-12127-2_28

DOI

http://dx.doi.org/10.1007/978-3-642-12127-2_28

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035478816


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