Density‐based clustering

By Ricardo José Gabrielli Barreto Campello, Peer Kröger, Jörg Sander, Arthur Zimek. WIREs Data Mining Knowl. Discov., 2019

578 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/fcfa5a4bee9780fd0433b912943db70334ff62d8

How researchers see this paper

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In Artificial Intelligence

1. Yuan-jie Li
2. An-Ding Xu
3. Gen Zhang
4. Aozi Feng
5. Joseph Homer Saleh
6. Xifeng Guo
7. Erxue Min
8. Tao Huang

In Signal Processing

1. Joseph Homer Saleh
2. Zhaoyi Xu
3. Derek Doran
4. Michael Hahsler
5. Qiang Liu
6. Matt Piekenbrock

Citations in the corpus

• Data mining in clinical big data: the frequently used databases, steps, and methodological models (2021, background): “Clustering according to density The density algorithm takes areas presenting a high degree of data density and defines these as belonging to the same cluster [76].”
• Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities (2020, methodology): “ found in Jain et al. [37] and Saxena et al. [38]. Clustering methods include the widely used K-means algorithm [39], a variety of hierarchical clustering methods [40], density-based clustering (DBC) [41], and Gaussian mixing model (GMM) [16]. With the recent development of DNN, there is a growing f”
• A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture (2018, methodology): “Classical clustering methods are usually categorized as partition-based methods [21], density-based methods [22], hierarchical methods [23] and so on.”
• dbscan: Fast Density-Based Clustering with R (2019, background): “It simply posits that clusters are contiguous ‘dense’ regions in the data space (i.e., regions of high point density), separated by areas of low point density (Kriegel, Kr¨oger, Sander, and Arthur 2011; Sander 2011).”
• Scalable text clustering based on word embeddings and noise analysis (2026, unknown): “Therefore, in the first step of the algorithm, we construct a minimum spanning tree of the graph, weighted by distance, using Prim’s algorithm [8], following the original HDBSCAN* formulation [9], in which Prim’s algorithm is used to compute the MST of the mutual reachability graph.”