At SIGMOD 2015, an article was presented with the title “DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation” that won the conference’s best paper award. In this technical correspondence, we want to point out some inaccuracies in the way DBSCAN was represented, and why the criticism should have been directed at the assumption about the performance of spatial index structures such as R-trees and not at an algorithm that can use such indexes. We will also discuss the relationship of DBSCAN performance and the indexability of the dataset, and discuss some heuristics for choosing appropriate DBSCAN parameters. Some indicators of bad parameters will be proposed to help guide future users of this algorithm in choosing parameters such as to obtain both meaningful results and good performance. In new experiments, we show that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest. In conclusion, the original DBSCAN algorithm with effective indexes and reasonably chosen parameter values performs competitively compared to the method proposed by Gan and Tao.

Clustering refers to the task of identifying groups or clusters in a data set. In density-based clustering, a cluster is a set of data objects spread in the data space over a contiguous region of high density of objects. Density-based clusters are separated from each other by contiguous regions of low density of objects. Data objects located in low-density regions are typically considered noise or outliers. In this review article we discuss the statistical notion of density-based clusters, classic algorithms for deriving a flat partitioning of density-based clusters, methods for hierarchical density-based clustering, and methods for semi-supervised clustering. We conclude with some open challenges related to density-based clustering.

Outlier detection research is currently focusing on the development of new methods and on improving the computation time for these methods. Evaluation however is rather heuristic, often considering just precision in the top k results or using the area under the ROC curve. These evaluation procedures do not allow for assessment of similarity between methods. Judging the similarity of or correlation between two rankings of outlier scores is an important question in itself but it is also an essential step towards meaningfully building outlier detection ensembles, where this aspect has been completely ignored so far. In this study, our generalized view of evaluation methods allows both to evaluate the performance of existing methods as well as to compare different methods w.r.t. their detection performance. Our new evaluation framework takes into consideration the class imbalance problem and offers new insights on similarity and redundancy of existing outlier detection methods. As a result, the design of effective ensemble methods for outlier detection is considerably enhanced.

Abstract