What are the benefits of K-means clustering over hierarchical clustering?

K-means clustering offers several advantages, one of which is that clusters do not have to be nested. This characteristic allows for a more flexible clustering approach, where each individual cluster can be formed independently based on the data points assigned to it. In contrast, hierarchical clustering produces a tree-like structure of clusters, where clusters are formed in a nested manner, often making it less flexible in handling different shapes and sizes of data.

The ability of K-means to create unique clusters without the need for a hierarchical structure facilitates applications where distinct and separate groups are essential for analysis. For instance, when dealing with large datasets that contain a high variety of patterns, K-means can effectively identify separate clusters that stand apart from one another, which may not be readily evident in hierarchical clustering.

Additionally, K-means is often easier to interpret in settings where the analysis seeks to identify a certain number of distinct groups, making it user-friendly for many practical applications. This independence from a nested structure or hierarchical arrangement allows K-means to adapt more readily to various datasets and clustering requirements.