This study was performed to examine the mechanism of flow-induced vibration (FIV) in a hard disk drive (HDD). For this purpose, data mining using self-organizing map (SOM) and Bayesian network was applied to unsteady computational fluid dynamics (CFD) simulation data for a hard disk drive. The present data mining started from the extraction of temporal indices from the time series data of fluid properties given at each grid point. Then, a set of grid points was divided into several clusters based on the similarity of the temporal indices by using SOM, and the clustered data were mapped onto a real space of HDD. Through this process, characteristic phenomena latent in the unsteady flow field were classified and identified. Finally, the relations between temporal indices and FIV were constructed by using Bayesian network. The resulting network structure revealed a possible mechanism ofFIV that originates from a temporal sequence of flow energy dissipation and production.