- Title
- Density-based clustering
- Creator
- Campello, Ricardo J. G. B.; Kröger, Peer; Sander, Jörg; Zimek, Arthur
- Relation
- WIREs Data Mining and Knowledge Discovery Vol. 10, Issue 2, no. e1343
- Publisher Link
- http://dx.doi.org/10.1002/widm.1343
- Publisher
- John Wiley & Sons
- Resource Type
- journal article
- Date
- 2020
- Description
- 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.
- Subject
- flat clustering; hierarchical clustering; nonparametric clustering; semi-supervised clustering; unsupervised clustering
- Identifier
- http://hdl.handle.net/1959.13/1432826
- Identifier
- uon:39118
- Identifier
- ISSN:1942-4787
- Language
- eng
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