International Journal of Computer
Trends and Technology

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Volume 27 | Number 1 | Year 2015 | Article Id. IJCTT-V27P121 | DOI : https://doi.org/10.14445/22312803/IJCTT-V27P121

Quad-Tree Based Multiple Kernel Fuzzy C-Means Clustering for Gene Expression Data

E. Monica Sushil Cynthia, S. Kannan

Citation :

E. Monica Sushil Cynthia, S. Kannan, "Quad-Tree Based Multiple Kernel Fuzzy C-Means Clustering for Gene Expression Data," International Journal of Computer Trends and Technology (IJCTT), vol. 27, no. 1, pp. 121-125, 2015. Crossref, https://doi.org/10.14445/22312803/IJCTT-V27P121

Abstract

Minute variations in genes can have a major impact on how humans respond to disease, environmental factors such as bacteria, viruses, toxins, chemicals and drugs and other therapies.. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. The clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. Hence in this paper we propose a new clustering algorithm for gene expression data associated to three different types of cancer and also compare with the existing approaches to prove the novel approach proposed here, has a better performance, reliability and provide more meaningful biological significance.

Keywords

Clustering, Clustering Algorithms, Gene Expression analysis, Fuzzy C-Means, Hierarchical Clustering, Gene Clustering, Gene Expression data, Quad Tree, Kernel fuzzy C-means.

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