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i am searching for an explaxation to this negative mentioned davies-bouldin values. Please, can anyone explain to me why Rapidminer ist calculation negative values?
My Performance Vectors are looking like this:
Average within centroid distance
cluster_0: -1.831 cluster_1: -1.931 cluster_2: -1.856 cluster_3: -1.897 cluster_4: -1.903 cluster_5: -1.885 cluster_6: -1.891 cluster_7: -1.878 cluster_8: -1.818 cluster_9: -1.869
Davides Bouldin: -1.974
Thanks in advance for your help and reply,
i do not know why, but by default the values are multiplied by -1 so that you can run a minimizer on it. That's why the operator has an option called maximize with this description:
Description: This parameter specifies if the results should be maximized. If set to true, the result is not multiplied by minus one.
Simply check it and get what you like more
thanks for your fast reply.
I read about the multiplication by -1. Thanks for the advanced paramrter advice. Now my values turn in positive ones. BUT, I am still wondering why the values are greater >1. Usualy Davies Boulding values are between 0 and 1 (0="good" clusters and 1="bad" clusters). Now that my values are greater 1, do you have a suggestions for interpretation?
why do you think this should be normalized? According to Wikipedia: https://en.wikipedia.org/wiki/Davies%E2%80%93Bouldin_index i don't see any reason to have it in [0,1].
Nevertheless you can of course normalize the DB index.
So in either case, does the most optimal cluster according to DB index have the resulting DB that is farthest from zero, or closest? Or asked another way, for the absolute value of DB is a DB index of 10 better or worse than a DB index of 1?
as far as i know smaller absolute values are better. From the doc:
davies_bouldin: The algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster distances (low inter-cluster similarity) will have a low Davies–Bouldin index, the clustering algorithm that produces a collection of clusters with the smallest Davies–Bouldin index is considered the best algorithm based on this criterion.