Table 1.

DRAGON Method

1. Given a sample set χ s (at the beginning χ s = χ), compute likelihood L (Eq. (12)).  2. Until L * > L, remove one sample $\widehat{\mathbf{x}}\in {\chi }_s$ (Eq. (18)), compute new likelihood L *, update χ s and L.  3. Find centroid μ s = E[χ s] and d x = δ(x, μ s) ∀ x ∈ χ.  4. Partition {d x} into two groups, for example using k-means algorithm (or divide into two groups based on their values). One of these groups will have lower d x values (representing closeness to μ s) whereas the other will have higher d x values (representing distance from μ s). Update χ s by replacing it with the samples with the lower d x values.  5. If required repeat steps 3 and 4. Take out the cluster χ s from χ. Update χ accordingly (the updated χ would contain all the samples except χ s; i.e. χ ∩ χ s = Φ).  6. Repeat all the steps until all the possible clusters (or desired number of clusters) are obtained.