Microarray Gene Expression Analysis Using Type 2 Fuzzy Logic (mga-fl)

Data mining is defined as the process of extracting or mining knowledge from vast and large database. Data mining is an interdisciplinary field that brings together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issue of information extraction from large databases. Bioinformatics is defined as the science of organizing and analyzing the biological data. Microarray technology helps biologists for monitoring expression of thousands of genes in a single experiment on a small chip. Microarray is also called as DNA chip, gene chip, or biochip is used to analyze the gene expression profiles. Fuzzy Logic is defined as a multivalued logic that provides the intermediate values to be defined between conventional evaluations like true or false, yes or no, high or low, etc.In this paper, a type 2 fuzzy logic approach is used in microarray gene expression data to convert the numerical values into fuzzy terms. After fuzzification, the fuzzy association patterns are discovered. A framework is proposed to cluster microarray gene data based on fuzzy association patterns. Then the proposed type 2 fuzzy approach is compared with traditional clustering algorithms.