An Effective Partitional Crisp Clustering Method Using Gradient Descent Approach

Autor:	Soroosh Shalileh
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	clustering objective functions clustering methods gradient descent approach Mathematics QA1-939
Zdroj:	Mathematics, Vol 11, Iss 12, p 2617 (2023)
Druh dokumentu:	article
ISSN:	11122617 2227-7390
DOI:	10.3390/math11122617
Popis:	Enhancing the effectiveness of clustering methods has always been of great interest. Therefore, inspired by the success story of the gradient descent approach in supervised learning in the current research, we proposed an effective clustering method using the gradient descent approach. As a supplementary device for further improvements, we implemented our proposed method using an automatic differentiation library to facilitate the users in applying any differentiable distance functions. We empirically validated and compared the performance of our proposed method with four popular and effective clustering methods from the literature on 11 real-world and 720 synthetic datasets. Our experiments proved that our proposed method is valid, and in the majority of the cases, it is more effective than the competitors.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/f1b994da944d4e5aaea7e8e2dcb1ce0f Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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