What is a potential downside of cluster analysis?

The selection of "Results can vary based on the algorithm used" identifies a key concern in cluster analysis. Different clustering algorithms, such as k-means, hierarchical clustering, or DBSCAN, employ various methods for grouping data points based on their similarities. This variability means that the same dataset can yield different cluster formations depending on the algorithm used, the chosen parameters, and initial settings like seed values in k-means.

For instance, k-means clustering is sensitive to the initial placement of cluster centroids and may converge to different solutions with different initializations. Hierarchical clustering may yield a different dendrogram structure based on the linkage criterion selected. Thus, this characteristic brings a level of subjectivity to the cluster identification process, impacting its reliability and repeatability across different applications.

In contrast, guarantees of natural clusters or computational efficiency are misleading within the context of cluster analysis, as no algorithm ensures natural grouping of the data, and computational efficiency can vary widely based on the size of the dataset and the specific algorithm used. Additionally, while some algorithms do not require specific distributional assumptions, this does not negate the variability in results tied to algorithm choice.