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Hierarchical clustering algorithms, e.g. Single-Link or OPTICS compute the hierarchical clustering structure of data sets and visualize those structures by means of dendrograms and reachability plots. Both types of algorithms have their own drawbacks. Single-Link suffers from the well-known single-link effect and is not robust against noise objects. Furthermore, the interpretability of the resulting dendrogram deteriorates heavily with increasing database size. OPTICS overcomes these limitations by using a density estimator for data grouping and computing a reachability diagram which provides a clear presentation of the hierarchical clustering structure even for large data sets. However, it requires a non-intuitive parameter ε that has significant impact on the performance of the algorithm and the accuracy of the results. In this paper, we propose a novel and efficient k-nearest neighbor join closest-pair ranking algorithm to overcome the problems of both worlds. Our density-link clustering algorithm uses a similar density estimator for data grouping, but does not require the ε parameter of OPTICS and thus produces the optimal result w.r.t. accuracy. In addition, it provides a significant performance boosting over Single-Link and OPTICS. Our experiments show both, the improvement of accuracy as well as the efficiency acceleration of our method compared to Single-Link and OPTICS.

DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking