I run k-means clustering on my dataset (100 samples in total) and partition the data into k=5 clusters. Then I want to test how robust of the k-means can be; however, I haven't got more new data samples. My idea is:
- Take the first sample out and run k-means on the rest of 99 samples.
- Loop over the step described above for each sample (e.g., take out the 2nd sample at the 2nd iteration), and run the k-means 100 times in total.
My question is how to measure the similarity of the 100 k-means results? I am thinking of get the statistics of silhouette coefficients. Does that make sense?