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Gene temporal expression data clustering has been widely used to study dynamic biological systems. However, most temporal gene expression data often contain noise, missing data points, and non-uniformly sampled time points, which imposes challenges for traditional clustering methods of extracting meaningful information. To improve the clustering performance, we introduce a novel clustering approach based on the continuous representations and an energy based similarity measure. The proposed approach models each gene expression profile as a B-spline expansion, for which the spline coefficients are estimated by regularized least squares scheme on the observed data. After fitting the continuous representations of gene expression profiles, we use an energy based similarity measure to take into account the temporal information and the relative changes of time series. Experimental results show that the proposed method is robust to noise and can produce meaningful clustering results.