The minimum fluid-flow velocity to ensure particle transport in pipelines is an essential design and operation consideration for oil and gas production. This flow velocity is difficult to estimate due to complex nature of the physical processes. It has been shown that the predictions of different, alternative models may vary several orders of magnitude for the same inputs. This paper introduces a systematic approach to reduce this discrepancy using data clustering, model selection, and cluster identification techniques. The approach is tested using 772 experimental data points (published in open literature), and the results show that the average of the error percentages between the predictions and experimental velocities are reduced from several orders of magnitude to 37%. (c) 2015 Elsevier Ltd. All rights reserved.