A novel scale-invariant, dynamic method for hierarchical clustering of data affected by measurement uncertainty.

An enhanced technique for hierarchical agglomerative clustering is presented. Classical clusterings suffer from non-uniqueness, resulting from the adopted scaling of data and from the arbitrary choice of the function to measure the proximity between elements. Moreover, most classical methods cannot account for the effect of measurement uncertainty on initial data, when present.