Why are my clusters not the same size?
If they vary, the clusters may not have the same size, or may be not well separated; and other algorithms may yield better results. I had a similar issue, but it’s that I wanted the data set from another distribution to be clustered the same way as the original data set.
Why do my clustering results vary from run to run?
In my opinion, when the results vary highly from run to run, this indicates that the data just does not cluster well with k-means at all. Your results are not much better than random in such a case. If the data is really suited for k-means clustering, the results will be rather stable!
How is a clustering algorithm used in statistics?
Figure 5.4: We decompose the choices made in a clustering algorithm according to the steps taken: starting from an observations-by-features rectangular table X X, we choose an observations-to-observations distance measure and compute the distance matrix, here schematized by the triangle. The distances are used to construct the clusters.
Can a same data set be clustered on two different machines?
The same data may be clustered as group 0 on one machine and clustered as group 1 on another machine. But at least with the same K-Means model ( cluster_maker in my code) we make sure data from another distribution will be clustered in the same way as the original data set.
What makes a cluster of calcifications more suspicious?
Distribution and number: clustered arrangements are more suspicious. If calcifications are clustered together or concentrated in one segment of the breast, they tend to be viewed with more concern. They might appear to be developing within a specific system of ducts or collecting in one segment of the breast.
Is the result of k-means clustering stable?
If the data is really suited for k-means clustering, the results will be rather stable! If they vary, the clusters may not have the same size, or may be not well separated; and other algorithms may yield better results.