I have a data set(numerical) and I created a k-means algorithm to create clusters based on the features. For K-means, since we assign the number of clusters, I wanted to try a different method and compare the results with k-means. For this I wrote a piece of code on agglomerative hierarchical clustering. Looking at my graphs and clusters with my naked eye, k -means and agglomerative graphs look the same. But that doesnt help to statistically say if it has any variance. Can anyone shed some light on how we could compare the 2 algorithms?
I hope this is not vague, please let me know if you need any specific details for this. I will be happy to post it. At this moment, I am stuck with the concept itself and wanted to throw the question only on that.
