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There are few well known measures like silhouette width (SW), the Davies- Bouldin index (DB), the Calinski-Harabasz index (CH), and the Dunn index . How can we say that a clustering quality measure is good?
Is there some kind of metric for the clustering quality measure to be good?

Also ,

"algorithms that produce clusters with high Dunn index are more desirable" -Wikipedia

"Objects with a high silhouette value are considered well clustered" -Wikipedia

"clustering algorithm that produces a collection of clusters with the smallest Davies–Bouldin index is considered the best algorithm" -Wikipedia

How high or low these values should be ?Is there a metric number ?

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If you don't have access to the ground truth (i.e. the cluster to which each data point belongs to), there's not much you can do. Those evaluation metrics are not meant to deliver an absolute value: they're relational. A low DB clustering is probably better than a high one.

If you have access to the ground truth, there are better -and bounded - evaluation metrics, like entropy, purity or, the best of all, clustering error (or maximum matching).

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