Which clustering method is noted for its shortcomings with circular or spiral data?

K-means clustering is known for its shortcomings with circular or spiral data primarily due to its reliance on distance measures, particularly Euclidean distance, and the assumption that clusters are spherical in shape. This method seeks to partition data into K distinct clusters by assigning points to the cluster whose centroid (average position) is nearest.

When the underlying data structure is circular or spiral, K-means tends to perform poorly. It often misclassifies points that are close to the center of a circular cluster, leading to suboptimal cluster formations. Since K-means uses centroids to define clusters, the algorithm can struggle to correctly represent the complexity and non-linear characteristics of circular or spiral patterns.

In contrast, other methods like DBSCAN can identify clusters of varying shapes, including circular ones, by focusing on density rather than distance to a centroid. Hierarchical clustering can create a dendrogram that encapsulates the structure of the data without imposing a rigid shape on the clusters. K-medoids, while similar to K-means, uses actual data points as centers, which can provide slightly better robustness to shape, but it also shares some limitations with predefined shape assumptions.

Therefore, the challenges K-means faces with non-spherical data shapes make it an unsuitable choice for