Relationship between clustering and data quality has not been thoroughly established. It is usually assumed that input dataset does not contain any errors or contains some “noise”, and this concept of “noise” is not related to any data quality concept. In this paper we focus on the four most commonly used data quality dimensions, namely accuracy, completeness, consistency and timeliness. We evaluate the impact these quality dimensions on clustering outcomes in order to find out which of them has the most negative effect. Four different clustering algorithms and five real datasets were selected to show the interaction between data quality and cluster validity.