Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data

By Levent Ertöz, Michael S. Steinbach, Vipin Kumar. SDM, 2003

785 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/366322db022a2fd79452110a0d5b353cfcfe3b79

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In Artificial Intelligence

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Citations in the corpus

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…”
A Survey of Clustering Data Mining Techniques (2006, methodology): “))log( samplesample N+( samplemNc 2N m The algorithm SNN (Shared Nearest Neighbors) [Ertoz et al. 2002] blends a densitybased approach with the idea of ROCK.”
A comprehensive survey of anomaly detection techniques for high dimensional big data (2020, background): “Ertoz et al. [63] introduced a shared nearest neighbor clustering algorithm to identify clusters of fluctuating shapes, sizes, and densities, along with the anomalies.”
A graph‐based approach to find teleconnections in climate data (2013, methodology): “As regions involving dipoles can be of different size, shape, and strength, we use a clustering scheme based on the SNN concept that is particularly effective in addressing such requirements [29].”
Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context (2003, positive): “Ertoz and Steinbach [6] used shared nearest neighbor (SNN) technique, which is particularly suited for finding clusters in data of different sizes, density, and shapes, mainlywhenthedatacontainslargeamountofnoiseand outliers.”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, background): “…[Hinneburg and Keim 1998, 2003; Hinneburg and Gabriel 2007], DECODE [Pei et al. 2009], and others [Coomans and Massart 1981; Comaniciu and Meer 1999; Ertöz et al. 2002, 2003; Stuetzle 2003; Sander et al. 2003; Brecheisen et al. 2004; Pascual et al. 2006; Stuetzle and Nugent 2010]; many are…”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “In [25], the performance of the shared nearest neighbor (SNN) based model in ID was studied and reported as the best algorithm with a high detection rate.”
Identification of cell types from single-cell transcriptomes using a novel clustering method (2015, methodology): “SNN techniques have been successfully applied to some clustering problems (Erto¨z et al. , 2003; Guha et al. , 2000; Jarvis and Patrick, 1973).”
Density‐based clustering (2019, methodology): “The challenge of finding density-based clusters with very different densities in a data set, which cannot be completely described by a single density level set, has been addressed, for example, by Ertöz, Steinbach, and Kumar (2003) and Pei, Jasra, Hand, Zhu, and Zhou (2009) for point data.”