  We describe in this paper the theory and practice behind a new modal
clustering method for binary data. Our approach (BinNNMS) is based on the
nearest neighbor median shift. The median shift is an extension of the
well-known mean shift, which was designed for continuous data, to handle binary
data. We demonstrate that BinNNMS can discover accurately the location of
clusters in binary data with theoretical and experimental analyses.
