Clustering gene data via Associative Clustering Neural Network

We describe a new approach to the analysis of gene expression data using Associative Clustering Neural Network (ACNN). ACNN dynamically evaluates similarity between any two gene samples through the interactions of a group of gene samples. It has feasibility to more robust performance than those similarities evaluated by direct distances. The clustering performance of ACNN has been tested on the Leukemias data set. The experimental results demonstrate that ACNN can achieve superior performance in high dimensional data ( 7129 genes). The performance can be further enhanced when some useful feature selection methodologies are incorporated. The study has shown ACNN can achieve 98.61% accuracy on clustering the Leukemias data set with correlation analysis.