How can we theoretically measure the performance of density-based clustering algorithms?

Many of clustering algorithms for a point cloud X n ⊂ ℝ d in the Euclidean space are based on density estimates [1]. In fact, the density function f of point generation contains the relevant information. It is quite natural to try to extract what Hartigan called 'high-density clusters' [2].