# What are the most common metrics for comparing two clustering algorithms (especially density based clustering) [closed]

When it comes to compare a new clustering algorithm, one always wants to show the advantages of his/her method over existing and well known methods. Going this way may mislead one to ignore disadvantages proposed method.

For clustering results, usually people compare different methods over a set of datasets which readers can see the clusters with their own eyes, and get the differences between different methods results.

There are some metrics, like Homogeneity, Completeness, Adjusted Rand Index, Adjusted Mutual Information, and V-Measure. To compute these metrics, one needs to know the true labels of data-set, so we may test algorithms with classification data-sets to have true labels and then evaluate results.

Another metrics, like Silhouette Coefficient works only with data and clustering results.

I want to know what measures are most preferred and if there is any other metric which does not require true labels of data-set.

• I think that the question raised is so broad... And perhaps you should make it more specific. One notion, however. Each clustering algorithm has its objective function, the goal; and it is how well it pursues its goal is the criterion. Goals are different because various definitions of what is cluster and what is density exist. – ttnphns Apr 30 '14 at 9:01
• There are many existing questions on existing clustering metrics. Even Wikipedia "knows" plenty of such measures. There is a whole bunch of literature on this, after all. – Has QUIT--Anony-Mousse May 1 '14 at 11:10
• @Anony-Mousse I've narrowed down the question. Is it still too broad? – Mehraban Jun 17 '14 at 12:09
• Does not help much: ''preference'' is subjective. Everybody seems to use a different metric. And there are plenty of internal evaluation measures such as Dunn and davis-bouldin index (see wikipedia), AIC, BIC, Silhouette, CdBW, ... that do not require labels. No measure is perfect. Each relies on assumptions that may or may not hold for your data. – Has QUIT--Anony-Mousse Jun 17 '14 at 13:21