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In many scientific, engineering or multimedia applications, complex distance functions are used to measure similarity accurately. Furthermore, there often exist simpler lower-bounding distance functions, which can be computed much more efficiently. In this paper, we will show how these simple distance functions can be used to parallelize the density-based clustering algorithm DBSCAN. First, the data is partitioned based on an enumeration calculated by the hierarchical clustering algorithm OPTICS, so that similar objects have adjacent enumeration values. We use the fact that clustering based on lower-bounding distance values conservatively approximates the exact clustering. By integrating the multi-step query processing paradigm directly into the clustering algorithms, the clustering on the slaves can be carried out very efficiently. Finally, we show that the different result sets computed by the various slaves can effectively and efficiently be merged to a global result by means of cluster connectivity graphs. In an experimental evaluation based on real-world test data sets, we demonstrate the benefits of our approach.