Self-organizing map with granular competitive learning: Application to microarray clustering

Abstract

Self-organizing map (SOM) models perform clustering process based on a competitive learning. The learning methods of these models involve neighborhood function such as Gaussian in the output layer, where the Euclidean distance from winning node to an output node is used. In this study, a granular competitive learning of SOM (SOMGCL) involving a fuzzy distance, the distance based granular neighborhood function and fuzzy initial connection weights is developed using the concepts of fuzzy rough set. The fuzzy distance between a winning node and an output node of SOM is computed where the average of memberships belonging to the lower approximations and boundary regions of a cluster obtained at the node is used. The fuzzy distance is incorporated into a Gaussian function to define the proposed neighborhood function. Dependency values of features using fuzzy rough sets are encoded into SOM as its fuzzy initial connection weights. Here, the concepts of fuzzy rough set are based on a new fuzzy strict order relation. While the fuzzy distance defines similarity measure in clustering process, the distance based granular neighborhood function handles uncertainty in cluster boundary regions. The effectiveness of SOMGCL is demonstrated in clustering of both the samples and genes in microarrays having the large number of genes and classes in terms of cluster evaluation metrics and quantization error. Further, biological meaning of gene clusters obtained using SOMGCL is elucidated using gene-ontology.