  Clustering partitions a dataset such that observations placed together in a
group are similar but different from those in other groups. Hierarchical and
$K$-means clustering are two approaches but have different strengths and
weaknesses. For instance, hierarchical clustering identifies groups in a
tree-like structure but suffers from computational complexity in large datasets
while $K$-means clustering is efficient but designed to identify homogeneous
spherically-shaped clusters. We present a hybrid non-parametric clustering
approach that amalgamates the two methods to identify general-shaped clusters
and that can be applied to larger datasets. Specifically, we first partition
the dataset into spherical groups using $K$-means. We next merge these groups
using hierarchical methods with a data-driven distance measure as a stopping
criterion. Our proposal has the potential to reveal groups with general shapes
and structure in a dataset. We demonstrate good performance on several
simulated and real datasets.
