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dbscan: Fast Density-Based Clustering with R
Journal of Statistical Software, 2019 This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced
Michael Hahsler +2 more
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A physical model inspired density peak clustering.
PLoS ONE, 2020 Clustering is an important technology of data mining, which plays a vital role in bioscience, social network and network analysis. As a clustering algorithm based on density and distance, density peak clustering is extensively used to solve practical ...
Hui Zhuang +3 more
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Harris’ Hawks Optimization-Tuned Density-based Clustering
Sukkur IBA Journal of Emerging Technologies, 2023 Clustering is a machine learning technique that groups data samples based on similarity and identifies outliers with distinct features. Density-based clustering outperforms other methods because it can handle arbitrary shapes of clustering distributions.
Kashif Talpur +5 more
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Boundary Detection-Based Density Peaks Clustering
IEEE Access, 2019 Clustering algorithms have a very wide range of applications on data analysis, such as machine learning, data mining. However, data sets often have problems with unbalanced and non-spherical distribution.
Dianfeng Qiao, Yan Liang, Lianmeng Jiao
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Semi-Supervised Density Peaks Clustering Based on Constraint Projection
International Journal of Computational Intelligence Systems, 2020 Clustering by fast searching and finding density peaks (DPC) method can rapidly identify the centers of clusters which have relatively high densities and high distances according to a decision graph. Various methods have been introduced to extend the DPC
Shan Yan +4 more
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Fast Component Density Clustering in Spatial Databases: A Novel Algorithm
Information, 2022 Clustering analysis is a significant technique in various fields, including unsupervised machine learning, data mining, pattern recognition, and image analysis.
Bilal Bataineh
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Source-lens clustering and intrinsic-alignment bias of weak-lensing estimators [PDF]
, 2013 We estimate the amplitude of the source-lens clustering bias and of the intrinsic-alignment bias of weak lensing estimators of the two-point and three-point convergence and cosmic-shear correlation functions.
Valageas, Patrick
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ConDPC: Data Connectivity-Based Density Peak Clustering
Applied Sciences, 2022 As a relatively novel density-based clustering algorithm, Density peak clustering (DPC) has been widely studied in recent years. DPC sorts all points in descending order of local density and finds neighbors for each point in turn to assign all points to ...
Yujuan Zou, Zhijian Wang
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Efficient incremental density-based algorithm for clustering large datasets
Alexandria Engineering Journal, 2015 In dynamic information environments such as the web, the amount of information is rapidly increasing. Thus, the need to organize such information in an efficient manner is more important than ever.
Ahmad M. Bakr +2 more
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Generalized density clustering
, 2010 We study generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is possible even in high dimensions.
Rinaldo, Alessandro, Wasserman, Larry
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