Design And Implementation Of Bioanalytical Software Based On Clustering Algorithm

Gene expression data technology is a cutting-edge biotechnology developed in recent years. It fundamentally changed the prospects of biomedical research, and it had a significant impact on the research of genomics and post-genome. Those large-scale biological data raised new challenges to exploratory analysis and information extraction. Clustering method is one of important gene expression data analytical methods, Common clustering algorithms include partition methods, hierarchical methods, density-based methods, and Biclustering algorithm which is popular recent years. The clustering method used in this paper is K-means algorithm. The algorithm is very suitable for a large number of data, however, it has shortcomings, this system improve for this.The main purpose of this system improves the original K-means algorithm, and optimizes the clustering results. The test data is about tumor gene expression which from the internet. At the same time, the use of JAVA technology to develop clustering analysis toolset software for this. The software contains two algorithms, one is the traditional K-means algorithm, and the other is the improvement of K-means algorithm, the results verify the validity of the application of the improved K-means algorithm.