I am conducting a cluster analysis involving 60 subjects and 5 continuous variables.
After appropriate scaling, I performed hierarchical clustering with Euclidean distance and complete linkage, and then k-means clustering with elbow point ispection and silhoutte width analysis relying on Euclidean distance.
Unfortunately, the results of these three different approaches to choose the appropriate number of clusters differ substantially. In particular, hierarchical clustering isolates 3 subjects early on, and thus at least 3 or 4 clusters appear appropriate. Elbow point inspection suggests that no single evident elbow is present. Conversely, silhoutte width is maximal with 2 clusters.
What should I do next? Is hierarchical preferable? I would trust more silhoutte width, at least in this case. Am I correct?