Nearest Neighbor Median Shift Clustering for Binary Data
This work addresses clustering for binary data, which is a domain-specific problem, and appears incremental as it extends the mean shift method to handle binary data.
The authors tackled the problem of clustering binary data by introducing BinNNMS, a new modal clustering method based on nearest neighbor median shift, and demonstrated its ability to accurately discover cluster locations through theoretical and experimental analyses.
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.