This work addresses the problem of detecting groups of observations (distributions) and flagging those that differ abnormally from the majority of the
groups, termed as atypical groups. The proposed method combines a hierarchical classification technique, to identify groups of similar distributions, with a
functional outlier detection method, to identify those groups that contain outliers. Groups with outlying observations are forwarded for sub clustering. Once
the final partition is obtained, each cluster is represented by a class prototype,
whose outlyingness is evaluated according to a functional approach. Clusters
with atypical class labels are flagged as atypical groups. The method is applied
for the detection of groups of atypical genomic words, based on their distances
distributions.publishe
