The paper introduces OPTICS, a new algorithm for cluster analysis that does not explicitly produce a clustering of a dataset but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information equivalent to density-based clusterings corresponding to a broad range of parameter settings, making it versatile for both automatic and interactive cluster analysis. The authors demonstrate how to extract traditional clustering information and the intrinsic clustering structure efficiently. For medium-sized datasets, the cluster-ordering can be represented graphically, and for large datasets, an appropriate visualization technique is introduced. The paper also discusses the effectiveness of existing clustering algorithms and the limitations of global parameter settings, highlighting the need for a more flexible approach. The OPTICS algorithm is compared with DBSCAN, and its performance is analyzed. The paper concludes with a discussion on future research directions.The paper introduces OPTICS, a new algorithm for cluster analysis that does not explicitly produce a clustering of a dataset but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information equivalent to density-based clusterings corresponding to a broad range of parameter settings, making it versatile for both automatic and interactive cluster analysis. The authors demonstrate how to extract traditional clustering information and the intrinsic clustering structure efficiently. For medium-sized datasets, the cluster-ordering can be represented graphically, and for large datasets, an appropriate visualization technique is introduced. The paper also discusses the effectiveness of existing clustering algorithms and the limitations of global parameter settings, highlighting the need for a more flexible approach. The OPTICS algorithm is compared with DBSCAN, and its performance is analyzed. The paper concludes with a discussion on future research directions.