Multi-objective optimization for clustering microarray gene expression data - a comparative study

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

3 Citations (Scopus)

Abstract

Clustering is one of the main data mining tasks. It can be performed on a fuzzy or a crisp basis. Fuzzy clustering is widely-applied with microarray gene expression data as these data are usually uncertain and imprecise. There are several measures to evaluate the quality of clustering, but their performance is highly related to the dataset to which they are applied. In a previous work the authors proposed using a multi-objective genetic algorithm – based method, NSGA – II, to optimize two clustering validity measures simultaneously. In this paper we use another multi-objective optimizer, NSPSO, which is based on the particle swarm optimization algorithm, to solve the same problem. The experiments we conducted on two microarray gene expression data show that NSPSO is superior to NSGA-II in handling this problem.

Original languageEnglish
Title of host publicationAgent and Multi-Agent Systems
Subtitle of host publicationTechnologies and Applications - 9th KES International Conference, KES-AMSTA 2015, Proceedings
EditorsLakhmi C. Jain, Gordan Jezic, Robert J. Howlett
PublisherSpringer Science and Business Media Deutschland GmbH
Pages123-133
Number of pages11
ISBN (Electronic)9783319197289
ISBN (Print)9783319197272
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes
Event9th KES International Conference on Agent and Multi-Agent Systems-Technologies and Applications - Sorrento, Italy
Duration: 17 Jun 201519 Jun 2015
Conference number: 9th

Publication series

NameSmart Innovation, Systems and Technologies
Volume38
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

Conference

Conference9th KES International Conference on Agent and Multi-Agent Systems-Technologies and Applications
Abbreviated titleKES-AMSTA 2015
CountryItaly
CitySorrento
Period17/06/1519/06/15

Keywords

  • Clustering
  • Microarray gene expression data
  • Multi-objective optimization

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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  • Cite this

    Muhammad Fuad, M. M. (2015). Multi-objective optimization for clustering microarray gene expression data - a comparative study. In L. C. Jain, G. Jezic, & R. J. Howlett (Eds.), Agent and Multi-Agent Systems: Technologies and Applications - 9th KES International Conference, KES-AMSTA 2015, Proceedings (pp. 123-133). (Smart Innovation, Systems and Technologies; Vol. 38). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-19728-9_10