A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationMultiple Classifier Systems - 9th International Workshop, MCS 2010, Proceedings
Pages274-283
Number of pages10
DOIs
Publication statusPublished - 14 May 2010
Externally publishedYes
Event9th International Workshop on Multiple Classifier Systems, MCS 2010 - Cairo, Egypt
Duration: 7 Apr 20109 Apr 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5997 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Workshop on Multiple Classifier Systems, MCS 2010
CountryEgypt
CityCairo
Period7/4/109/4/10

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Keywords

  • Arbitrary shaped clusters
  • Clustering
  • Density based
  • Distance-relatedness based
  • Gene expression analysis
  • Multi-objective clustering
  • Sequential ensemble

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yousri, N. (2010). A multi-objective sequential ensemble for cluster structure analysis and visualization and application to gene expression. In Multiple Classifier Systems - 9th International Workshop, MCS 2010, Proceedings (pp. 274-283). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5997 LNCS). https://doi.org/10.1007/978-3-642-12127-2-28