Density‐based clustering

474 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.

Machine learning for Internet of Things data analysis: A survey (2017, unknown): “In this model, groups of dense data points (data points with many close neighbors) are considered as clusters and data points in regions with low-density are considered as outliers [80].”
An Introduction to Machine Learning (2020, background): “19 In density-based methods, a cluster represents a part of the feature space where data points are dense.”
Density‐based clustering (2019, background): “1This is an update of the previous article byKriegel et al. (2011). Besidesminor updates throughout, some refined explanations, and restructuring, we have the following major additions: (i) more thorough discussion of hierarchical clustering, including the latest state of the art; (ii) a new section”
The structure of online social networks mirrors those in the offline world (2015, background): “…between two adjacent clusters might cause the algorithm to treat them all as a single cluster instead of two (the so called “single link effect”: Kriegel et al. 2011), while (ii) the presence of a large number of noisy points in the data set could lead to the detection of more clusters than…”