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Clustering data has been an important task in data analysis for years as it is now. The de facto standard algorithm for density-based clustering today is DBSCAN. The main drawback of this algorithm is the need to tune its two parameters and minPts. In this paper we explore the possibilities and limits of two novel different clustering algorithms. Both require just one DBSCAN-like parameter. Still they perform well on benchmark data sets. Our first approach just uses a parameter similar to DBSCAN's minPts parameter that is used to incrementally find protoclusters which are eventually merged while discarding those that are too sparse. Our second approach only uses a local density without any minimum number of points to be specified. It estimates clusters by seeing them from spectators watching the data points at different angles. Both algorithms lead to results comparable to DBSCAN. Our first approach yields similar results to DBSCAN while being able to assign multiple cluster labels to a points while the second approach works significantly faster than DBSCAN.