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Campello, Ricardo J. G. B.; Kröger, Peer; Sander, Jörg; Zimek, Arthur (2019): Density-based clustering. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, No. 2, e1343
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Abstract

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. This article is categorized under: Technologies > Data Preprocessing Ensemble Methods > Structure Discovery Algorithmic Development > Hierarchies and Trees