Synthetic data generators are one of the methods used in privacy preserving data mining for ensuring the privacy of the individuals when their data are published. Synthetic data generators construct artificial data from some models obtained from the original data. Such models are mainly based on statistics and, typically, do not take into account other aspects of interest in artificial intelligence. In this paper we study whether one family of such synthetic data generators (the IPSO family) preserves the properties of the data that are of interest when users plan to apply clustering techniques. In particular, we study the effect of such synthetic data generators on fuzzy clustering. That is, we study the information loss data suffer when the original data are replaced by the synthetic ones.