# Using Davies-Bouldin index in clustering

I am clustering data using k-medoid. I used Davies–Bouldin index for $2$ to $n-1$ clusters. Here $n = 100$ (using smaller test case). I find minimal value of the index for 98 clusters. But the overall accuracy rate for 98 cluster is very small (smaller than 1). Here accuracy rate is how accurately test data is matched to training data. What should I do in that situation. If dataset is larger then finding Davies–Bouldin value from $2$ to $n-1$ is large task. What should I do for larger dataset?

Here is my plot of Davies–Bouldin index value for cluster solutions [X axis "index" is actually the number of clusters in a solution].

• Please post here the curve of DB values of 100- to 2-cluster solutions, so we can see. Also, this post might be informative reading. Apr 13, 2013 at 21:41
• @ttnphns I added my DB value plot Apr 13, 2013 at 22:21
• The index suggests you 4 or 5 clusters (i.e. 3rd or 4th point from the left). I recommend you to check also other indices, if you have time, - e.g. Calinski-Harabasz (which is similar to Davies-Bouldin) or point-biserial correlation or C-Index or Silhouette index (which are not based on ANOVA ideology). Apr 13, 2013 at 22:38
• @ttnphns db_value for cluster 4 is 3.661918 and cluster 98 is 0.020936. Can davis-bouldin differ from other methods ? Apr 13, 2013 at 23:03
• Mate, FYI, it is Davies–Bouldin, not Davis. Apr 14, 2013 at 1:50