Density-based clustering of uncertain data

By Hans-Peter Kriegel, M. Pfeifle. Knowledge Discovery and Data Mining, 2005

369 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Uncertain Data Algorithms and Applications (2009, methodology): “Based on this technique and the distance density function, the method in [51] defines a reachability probability between two data points.”
dbscan: Fast Density-Based Clustering with R (2019, methodology): “These clustering algorithms are widely used in practice with applications ranging from finding outliers in datasets for fraud prevention (Breunig, Kriegel, Ng, and Sander 2000), to finding patterns in streaming data (Chen and Tu 2007; Cao, Ester, Qian, and Zhou 2006), noisy signals (Kriegel and Pfei”
The DARPA Twitter Bot Challenge (2016, positive): “ In contrast to USC, Sentimetrix used the well-‐known DBSCAN [15] algorithm to generate clusters and then prioritized which users in these clusters were likely to be bots through an analysis of their features and the similarities between those features and”