  Commonly-used clustering algorithms usually find ellipsoidal, spherical or
other regular-structured clusters, but are more challenged when the underlying
groups lack formal structure or definition. Syncytial clustering is the name
that we introduce for methods that merge groups obtained from standard
clustering algorithms in order to reveal complex group structure in the data.
Here, we develop a distribution-free fully-automated syncytial clustering
algorithm that can be used with $k$-means and other algorithms. Our approach
estimates the cumulative distribution function of the normed residuals from an
appropriately fit $k$-groups model and calculates the estimated nonparametric
overlap between each pair of clusters. Groups with high pairwise overlap are
merged as long as the estimated generalized overlap decreases. Our methodology
is always a top performer in identifying groups with regular and irregular
structures in several datasets and can be applied to datasets with scatter or
incomplete records. The approach is also used to identify the distinct kinds of
gamma ray bursts in the Burst and Transient Source Experiment 4Br catalog and
the distinct kinds of activation in a functional Magnetic Resonance Imaging
study.
