[PDF][PDF] Adaptive Gaussian Density Distance for Clustering
M Yazdian-Dehkordi, F Nadi… - Tabriz Journal of …, 2022 - journals.tabrizu.ac.ir
Distance-based clustering methods categorize samples by optimizing a global criterion,
finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering
techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most
of these methods have several hyper-parameters, and their performance is highly
dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD)
approach was proposed to optimize local criteria in terms of distance and density properties …
finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering
techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most
of these methods have several hyper-parameters, and their performance is highly
dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD)
approach was proposed to optimize local criteria in terms of distance and density properties …
Adaptive Gaussian Density Distance for Clustering
مهدی یزدیان دهکردی, فرزانه نادی, فرزانه… - مجله مهندسی برق دانشگاه …, 2022 - tjee.tabrizu.ac.ir
Distance-based clustering methods categorize samples by optimizing a global criterion,
finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering
techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most
of these methods have several hyper-parameters, and their performance is highly
dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD)
approach was proposed to optimize local criteria in terms of distance and density properties …
finding ellipsoid clusters with roughly equal sizes. In contrast, density-based clustering
techniques form clusters with arbitrary shapes and sizes by optimizing a local criterion. Most
of these methods have several hyper-parameters, and their performance is highly
dependent on the hyper-parameter setup. Recently, a Gaussian Density Distance (GDD)
approach was proposed to optimize local criteria in terms of distance and density properties …
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