Nearest Neighbor Median Shift Clustering for Binary Data

Add code
Feb 11, 2019
Gaël Beck, Tarn Duong, Mustapha Lebbah, Hanane Azzag
Figure 1 for Nearest Neighbor Median Shift Clustering for Binary Data
Figure 2 for Nearest Neighbor Median Shift Clustering for Binary Data
Figure 3 for Nearest Neighbor Median Shift Clustering for Binary Data
Figure 4 for Nearest Neighbor Median Shift Clustering for Binary Data

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

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.

* Algorithms are available at https://github.com/Clustering4Ever/Clustering4Ever  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon