Computational geometry: an introduction
Computational geometry: an introduction
Algorithms for clustering data
Algorithms for clustering data
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Incremental distance join algorithms for spatial databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
A Cost Model and Index Architecture for the Similarity Join
Proceedings of the 17th International Conference on Data Engineering
The k-Nearest Neighbour Join: Turbo Charging the KDD Process
Knowledge and Information Systems
Gorder: an efficient method for KNN join processing
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
A unique property of single-link distance and its application in data clustering
Data & Knowledge Engineering
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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.