Introduction

A new granular self-organizing map (GSOM) is developed by integrating the concept of a fuzzy rough set with the SOM. While training the GSOM, the weights of a winning neuron and the neighborhood neurons are updated through a modified learning procedure. The neighborhood is newly defined using the fuzzy rough sets. The clusters (granules) evolved by the GSOM are presented to a decision table as its decision classes. Based on the decision table, a method of gene selection is developed. The effectiveness of the GSOM is shown in both clustering samples and developing an unsupervised fuzzy rough feature selection (UFRFS) method for gene selection in microarray data. While the superior results of the GSOM, as compared with the related clustering methods, are provided in terms of β -index, DB-index, Dunn-index, and fuzzy rough entropy, the genes selected by the UFRFS are not only better in terms of classification accuracy and a feature evaluation index, but also statistically more significant than the related unsupervised methods. The C-codes of the GSOM and UFRFS are available online at http://avatharamg.webs.com/software-code.

Publications

  1. A Granular Self-Organizing Map for Clustering and Gene Selection in Microarray Data.
    Cite this
    Ray SS, Ganivada A, Pal SK, 2016-09-01 - IEEE transactions on neural networks and learning systems

Credits

  1. Shubhra Sankar Ray
    Developer

  2. Avatharam Ganivada
    Developer

  3. Sankar K Pal
    Investigator

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Summary
AccessionBT003762
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesC
User InterfaceTerminal Command Line
Download Count0
Submitted BySankar K Pal