An Improved Clustering Technique Based on Statistical Model Preprocessing Using Gene Expression Data

Micro arrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this work, a comparison of performance of several feature selection methods based on data preprocessing including strategies of normalization or data reduction is studied and a new classical statistic technique is proposed for preprocessing. Then clustering technique is applied and promising results were achieved. The work also proves choice of a good preprocessing technique prior to clustering improves the performance. The results were proven to be the best in comparison with previous work.