# Nearest Neighbor Median Shift Clustering for Binary Data

## TL;DR

This paper introduces BinNNMS, a novel clustering method for binary data based on nearest neighbor median shift, extending mean shift techniques to handle binary datasets effectively.

## Contribution

The paper presents BinNNMS, a new modal clustering algorithm specifically designed for binary data, with theoretical foundations and practical validation.

## Key findings

- Accurately locates clusters in binary data
- Theoretically supported clustering performance
- Validated through experimental analysis

## Abstract

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.