MGKA: A genetic algorithm-based clustering technique for genomic data

Advances in high-throughput technologies have generated enormous amounts of high-throughput genomic data. Cluster analysis is often the first step to gain insights into genomic data. K-means, the most widely used clustering algorithm, is known to produce sub-optimal clusters depending on the choice of initialized centers. In this paper, we propose a genetic algorithm-based unsupervised clustering method that searches for the optimal centers of clusters based on the concept of k-means. The genetic algorithm reduces k-means sensitivity to randomly initialized centers and reduces the probability of converging to local minima. Two clustering validity indexes are introduced to the selection process to automatically determine the appropriate number of clusters. The proposed algorithm is applied to 16 disease datasets and four single-cell datasets to demonstrate its performance. Results show that our approach outperforms the current state of the art algorithms on a majority of the datasets.