  Bursts of the count rate of extensive air showers (EAS) lead to the
appearance of clusters in time series that represent EAS arrival times. We
apply methods of nonlinear time series analysis to twenty EAS cluster events
found in the data set obtained with the EAS-1000 prototype array. In
particular, we use the Grassberger-Procaccia algorithm to compute the
correlation dimension of the time series in the vicinity of the clusters. We
find that four cluster events produce signs of chaos in the corresponding time
series. By applying a number of supplementary methods we assess that the nature
of the observed behaviour of the correlation dimension is likely to be
deterministic. We suggest a simple qualitative model that might explain an
origin of clusters in general and "possibly chaotic" clusters in particular.
Finally, we compare our conclusions with the results of similar investigations
performed by the EAS-TOP and LAAS groups.
