In this study on a long-span suspension bridge, the wind and vibration data collected by a long-term monitoring system from the year 2010 to 2014 are used for cluster analysis. An automatic and fast clustering algorithm is employed to recognize the vortex-induced vibrations (VIVs) on the bridge deck in long-term monitoring datasets. The acceleration amplitude (root mean square) and frequency ratio are selected as indicators for the clustering of the VIVs from other vibrations. Cluster analysis is further conducted on the wind speed field of VIV samples, arid the results indicate that the nonuniformity of the wind speed along the span-wise direction has a significant influence on the VIV mode. The relationship between the wind speed field and VIV mode is obtained by the clustering with consideration of nonuniformity of the wind speed. (C) 2017 Elsevier Ltd. All rights reserved.