Advances in Computational Systems Biology

Lodhi, Huma

doi:10.1002/9780470556757.ch1

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Fuzzy Clustering Of Microarray Data Using Multi-objective Op1

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2015

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“…Gene microarray is a powerful tool to study the regulatory relationship between genes. The huge amount of data generated by gene microarray experiments is explored to answer fundamental questions about gene functions and their interdependence [13].…”

Section: Fuzzy Clustering Of Microarray Data Using Multi-objective Opmentioning

confidence: 99%

Multi-objective Optimization for Clustering Microarray Gene Expression Data - A Comparative Study

Fuad

2015

Agent and Multi-Agent Systems: Technologies and Applications

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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.

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Section: Fuzzy Clustering Of Microarray Data Using Multi-objective Opmentioning

confidence: 99%

Multi-objective Optimization for Clustering Microarray Gene Expression Data - A Comparative Study

Fuad

2015

Agent and Multi-Agent Systems: Technologies and Applications

View full text Add to dashboard Cite

show abstract

Advances in Computational Systems Biology

Cited by 1 publication

References 60 publications

Multi-objective Optimization for Clustering Microarray Gene Expression Data - A Comparative Study

Multi-objective Optimization for Clustering Microarray Gene Expression Data - A Comparative Study

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