  Data stream mining extracts information from large quantities of data flowing
fast and continuously (data streams). They are usually affected by changes in
the data distribution, giving rise to a phenomenon referred to as concept
drift. Thus, learning models must detect and adapt to such changes, so as to
exhibit a good predictive performance after a drift has occurred. In this
regard, the development of effective drift detection algorithms becomes a key
factor in data stream mining. In this work we propose CU RIE, a drift detector
relying on cellular automata. Specifically, in CU RIE the distribution of the
data stream is represented in the grid of a cellular automata, whose
neighborhood rule can then be utilized to detect possible distribution changes
over the stream. Computer simulations are presented and discussed to show that
CU RIE, when hybridized with other base learners, renders a competitive
behavior in terms of detection metrics and classification accuracy. CU RIE is
compared with well-established drift detectors over synthetic datasets with
varying drift characteristics.
