GDD Clustering

Distance and density based clustering algorithm using Gaussian kernel

You can access the paper using the link below:

https://www.sciencedirect.com/science/article/abs/pii/S095741741630553X

Figure 1 - Example of clustering process for 1D data.

Properties

• No parameter is needed
• Similarities are grouped together using Gaussian kernel and distances (see Figure 1)
• Resulting clusters do not change at different runs.

[Please cite as]:

Emre Güngör, Ahmet Özmen, Distance and density based clustering algorithm using Gaussian kernel, In Expert Systems with Applications, Volume 69, 2017, Pages 10-20, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2016.10.022. (http://www.sciencedirect.com/science/article/pii/S095741741630553X)

for clustering datasets and or shapesets, you can look;
https://cs.joensuu.fi/sipu/datasets/
(Note: You may get security warning from your browsers).