# Clustering - Different algorithms, same results

I'm working on my first clustering assignement and I've ran K-Means, Spectral clustering, Hierarchical clustering and Mini-Batch K-Means on same data and received the exact same results (cluster sizes, Calinski-Harabasz criterion, Silhouette). Does anyone have any idea, why would this be? Could this be only dependant on my dataset (6 variables, 2400 entities)?
Initialization of algorithms:

k_means = KMeans(init='k-means++', n_clusters=2)

mini_batch_k_means = MiniBatchKMeans(init='k-means++', n_clusters=2,n_init = 10, max_no_improvement = 10)

spectral_clustering = SpectralClustering(n_clusters=2)


EDIT: Correction, cluster sizes, Silhouette and CH index are the same, but not the actual clusters. The actual clusters are just extremely similar with some differences on borders. As suggested I projected my data points to 3D plane (with PCA), but as you can see from the image below, there is no clear distinction of the two clusters.

Curiously enough, when I made a decision tree on clustered data (for better visualization), I noticed that the decision tree only uses one variable to classify to which cluster does the data belong.

• Correction, cluster sizes, Silhouette and CH index are the same, but not the actual clusters. The actual clusters are just extremely similar. – Luka Žontar Dec 8 '19 at 0:21
• Welcome to the site. Regarding your correction, it's best to edit this into your question, rather than post it as a comment. This way, the question will be self-contained and people reading it can understand the situation more easily. – user20160 Dec 8 '19 at 3:35
• If your data has very obvious, well separated clusters, all the methods tens to work. Differences become more pronounced when you have difficult data with noise, non-convex clusters, overlapping clusters, large size and density differences of clusters, etc. – Has QUIT--Anony-Mousse Dec 8 '19 at 8:22
• I edited the question, thank you for your suggestion! @Anony-Mousse thank you for your answer! – Luka Žontar Dec 8 '19 at 9:02
• When the scores are "the same", maybe they are equally bad? A Silhouette of 0.2 does not mean much, if anything... But maybe also your evaluation code is wrong. – Has QUIT--Anony-Mousse Dec 8 '19 at 23:49