The program camera_identification.py currently uses scipy.cluster.hierarchy to create a dendogram. A flat clustering is extracted from the dendogram using a particular threshold. The problem is that the threshold to use is related to the expected distances between the clusters in the dataset. Ideally we would like to have some way of dynamically determining the threshold to use. This can be a lot work, as it could mean that we have to implement our own algorithm for computing the linkage. In the end that means we are implementing our own hierarchical clustering algorithm.
The program camera_identification.py currently uses scipy.cluster.hierarchy to create a dendogram. A flat clustering is extracted from the dendogram using a particular threshold. The problem is that the threshold to use is related to the expected distances between the clusters in the dataset. Ideally we would like to have some way of dynamically determining the threshold to use. This can be a lot work, as it could mean that we have to implement our own algorithm for computing the linkage. In the end that means we are implementing our own hierarchical clustering algorithm.