Which is a limitation of k-means clustering?

One of the main limitations of k-means clustering is that it requires the analyst to pre-specify the number of clusters they wish to identify in the dataset. This is a significant drawback because determining the optimal number of clusters can be quite challenging, especially if there is little prior knowledge about the data. If the value chosen for the number of clusters is inappropriate, it can lead to poor clustering results, with either too few clusters oversimplifying the data or too many clusters creating unnecessary complexity.

In contrast, other clustering methods may not require the number of clusters to be defined in advance, allowing them to adapt more flexibly to the data's inherent structure. This characteristic underscores the importance of careful consideration when using k-means, as the choice of the number of clusters directly influences the effectiveness of the clustering and can impact subsequent analyses or interpretations drawn from the model's results.