I achieved the exact shape of clusters using KMeans! But I wonder which metric I should use to find the optimal number of clusters! I used silhouette coefficient, But it results in 2 clusters for these kinds of scattered data! So I'm slightly suspicious if I used a suitable metric for this problem.
To better explain this case, I want to dig into some details. One may ask what the
x ticks and
y ticks are.
x ticksare the index of data! like
[1, 2, ... length of data]
y ticksshows the distance of each data point from a specific one.
So now it's a little bit clear what the idea is. I want data points within a distance range of (0, 🔴) to be in the same cluster, and distance range of (🔴, 🟡) in the same cluster, and so on. But the problem is I don't trust the silhouette coefficient because it always shows me 2 clusters through all varieties of data that I have in this specific problem!
the shape of clusters should be like the example (this is intended), no matter how scattered the data is.