A common question asked about unlabeled data sets is how many subsets (or clusters) of objects are represented in the data? The answer to this question is usually obtained by first clustering the data, and then employing a cluster validity measure to validate one or more candidate partitions of the objects. In this paper we describe an universal cluster validity measure that, unlike most existing measures, can be applied to partitions obtained by any relational or object data clustering algorithm. We illustrate the new measure, and compare it to several well known existing measures using a variety of artificial data sets.