Temporal clustering of time series data is a powerful tool to delaminate the dynamics of transcription and interactions among genes on a large scale. Different algorithms have been proposed to organize experimental data with meaningful biological clusters; however, these approaches often fail to generate well-defined temporal clusters, especially when genes exert their functions or response to stimulation coordinately only in a short time span. In this study, we propose an algorithm using sliding windows to identify different temporal patterns based on fold changes of gene expression. The algorithm was applied to simulated data and real experimental data. Furthermore, a comparison study has been carried out with the clusters obtained from commercial software packages. The identified clusters using our algorithm demonstrated better temporal matching and consistency.