Nearest Neighbor Median Shift Clustering for Binary Data

Gaël Beck, Tarn Duong, Mustapha Lebbah, Hanane Azzag

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.

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