  We propose Enhash, a fast ensemble learner that detects \textit{concept
drift} in a data stream. A stream may consist of abrupt, gradual, virtual, or
recurring events, or a mixture of various types of drift. Enhash employs
projection hash to insert an incoming sample. We show empirically that the
proposed method has competitive performance to existing ensemble learners in
much lesser time. Also, Enhash has moderate resource requirements. Experiments
relevant to performance comparison were performed on 6 artificial and 4 real
data sets consisting of various types of drifts.
