  Neuromemristive systems (NMSs) currently represent the most promising
platform to achieve energy efficient neuro-inspired computation. However, since
the research field is less than a decade old, there are still countless
algorithms and design paradigms to be explored within these systems. One
particular domain that remains to be fully investigated within NMSs is
unsupervised learning. In this work, we explore the design of an NMS for
unsupervised clustering, which is a critical element of several machine
learning algorithms. Using a simple memristor crossbar architecture and
learning rule, we are able to achieve performance which is on par with MATLAB's
k-means clustering.
