Incremental genetic K-means algorithm and its application in gene expression data analysis

Background: In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results: In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA). IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm (FGKA). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variat...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Yi Lu, Shiyong Lu, Farshad Fotouhi, Youping Deng, Susan J. Brown
• Keywords: Algorithms, BMCBI 2004, Gene Expression Data, Molecular Biology