ST-DBSCAN: An algorithm for clustering spatial-temporal data

By Derya Birant, Alp Kut. 2007

1,453 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Signal Processing

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Citations in the corpus

Memory Efficient Experience Replay for Streaming Learning (2018, background): “Density-based algorithms cluster data that lie in highdensity regions of feature space together, while labeling points in low-density regions as outliers [8], [10], [15], [29], [47].”
dbscan: Fast Density-Based Clustering with R (2019, background): “From identifying causes among similar seawater characteristics (Birant and Kut 2007), to network intrusion detection systems (Ert¨oz, Steinbach, and Kumar 2003), point of interest detection using geo-tagged photos (Kisilevich et al. 2010), classifying cancerous skin lesions (Celebi, Aslandogan, and…”
Big data for cyber physical systems in industry 4.0: a survey (2019, methodology): “DBSCAN (Ester et al. 1996) is the ﬁ rst method in this type, and other common methods in this type include ST-DBSCAN (Birant and Kut 2007), LDBSCAN (Duan et al. 2007), and OPTICS (Ankerst et al. 1999).”
A review of moving object trajectory clustering algorithms (2016, background): “Only Birant and Kut (2007) includes cyclic time patterns, and Nanni and Pedreschi (2006) considers time intervals.”
A clustering-based approach for discovering interesting places in trajectories (2008, neutral): “ST-DBSCAN [4] was developed to consider both spatial and temporal aspects, but it treats them separately in the evaluation function.”
Spatio-Temporal Data Mining (2017, background): “ST-DBSCAN [Birant and Kut 2007] is one of the popular extensions of DBSCAN that defines two separate distances between ST points: one that captures spatial attributes and another that captures temporal and non-ST attributes.”
DBSCAN: Past, present and future (2014, methodology): “[23] improved the DBSCAN algorithm by introducing a new density-based clustering algorithm called ST-DBSCAN.”
A Comprehensive Survey of Clustering Algorithms (2015, methodology): “For more detailed information about this kind of clustering algorithms, you can refer to [119–122], ST-DBSCAN [123].”
Clustering Algorithms Research (2008, neutral): “2007 年,Derya 等人([36])对 DBSCAN(density-based spatial clustering of applications with noise)进行了与辨识”