Merging K‐means with hierarchical clustering for identifying general‐shaped groups

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.