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) hierarchical_clustering = AgglomerativeClustering(n_clusters=2, linkage='ward') spectral_clustering = SpectralClustering(n_clusters=2)
Thank you for your answers!
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.