We have proposed a novel data mining method called co-occurring cluster mining (CCM) for mining patterns from a sequence of multidimensional event data. The CCM first generates cluster candidates and then test the candidates based on clustering in the data space as well as co-occurrence degree in the event sequence. In searching appropriate clusters associated with co-occurrence, the search space is reduced by obtaining a dendrogram from a hierarchical clustering as the clustering procedure. In this paper, we show the potential of CCM with following two applications: (1) damage patterns in fuel cell and (2) earthquake occurrence patterns. In the fuel cell application, given a sequence of acoustic emission events, which comprise of waveform signal data of damages to a fuel cell, the mechanical interactions between components of the fuel cell are inferred from the mined co-occurrence patterns. Similarly, in the application of earthquakes, the interactions between distant earthquakes are extracted as co-occurrence patterns from a hypocenter catalog.