I am just wondering about this issue brought up by our teacher about a drawback of K-means being unable to detect small clusters. It's homework that we should come up with ideas about why this is so and propose a solution to address it.
I know that K-means has an assumption that each cluster should have roughly the same number of observations, so perhaps if say 3 clusters have ~1000 observations, the 4th one "shouldn't" have 10 observations? Am I right in saying that?
I read Clustering: k-means alternatives when its assumptions do not hold and Is it true that K-Means has an assumption "each cluster has a roughly equal number of observations"? and lastly this How to understand the drawbacks of K-means.
Is it something to do with minimizing the SSE that causes this issue? Any insights are appreciated.
