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I'm wondering what are the most common/recognized methods to assess the quality of a clustering.
That is because I have developed a tool that can cluster/partition a network (in this case, a public transport network represented by a GTFS feed) in several different manners (graph partitioning, hierarchical clustering, etc ...) and my 'real' metric is almost impossible to get.
In brief, the 'real' metric would be the speed-up obtained using a cluster-based searched against the non cluster-based search. This depends on too many factors (input distribution, implementation of the search algorithms, architecture, etc ....).

I have developed a small function that can evaluate the quality of a clustering using the ratio of vertices of the border of a cluster, against the total amount of vertices. That is because it makes sense, intuitively, with my problem. But there is no mathematic foundation behind it.

My question is: What are the most common methods to assess the quality of a clustering/partitioning ?

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  • $\begingroup$ Have you taken a look at recent papers and how they do it? $\endgroup$ – dkaeae Jul 9 at 11:44
  • $\begingroup$ @dkaeae For graph partitioning, yes, since the problem definition gives also a mean to evaluate the quality of its solution (like minimizing the weight of the cut edges, or conductance ....). As for clustering, I did not find a good survey that describe several evaluation methods. I guess it's because there is a big variety of problems to solve, and approaches, under the name 'clustering algorithm'. $\endgroup$ – m.raynal Jul 9 at 11:59
  • $\begingroup$ Searching a bit deeper, I found that this could be a duplicate of this question, and that there are several relevant answers on that link. It provides several measures already. $\endgroup$ – m.raynal Jul 9 at 13:31

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