eXploratory K-Means: A new simple and efficient algorithm for gene clustering

In this paper, a novel gene expression clustering method known as eXploratory K-Means (XK-Means) is proposed. The method is based on the integration of the K-Means framework, and an exploratory mechanism to prevent premature convergence of the clustering process. Experimental results reveal that the performance of XK-Means in grouping gene expressions, measured in terms of speed, error and stability, is superior to existing methods that are based on evolutionary algorithm. In addition, the complexity of the proposed method is lower and the method can be easily implemented in practice.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► The XK-Means method in gene clustering is superior to existing methods. ► The proposed method can balance the high and low density of the clusters better. ► The proposed method is simple and can be easily implemented in practice.