An improved clustering method based on biological visual models

DOI10.1016/j.apm.2020.04.008zbMath1481.68040OpenAlexW3023858501WikidataQ113880214 ScholiaQ113880214MaRDI QIDQ2049719

Bernardo Morales-Castañeda, Alma Rodríguez, Gerardo García-Gil, Erik Cuevas, Daniel Zaldivar, Marco Perez-Cisneros

Publication date: 27 August 2021

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.apm.2020.04.008

zbMATH Keywords

clusteringrandom searchhill-climbingcellular neural/nonlinear networksdensity clustering methods

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Approximation methods and heuristics in mathematical programming (90C59) Neural networks for/in biological studies, artificial life and related topics (92B20) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Uses Software

• UCI-ml
• clusfind
• CNN

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Edge detection of noisy images based on cellular neural networks
• Self-organized formation of topologically correct feature maps
• An image segmentation method based on network clustering model
• Fast and effective cluster-based information retrieval using frequent closed itemsets
• Random optimization
• Cellular neural networks: theory
• Finding Groups in Data

This page was built for publication: An improved clustering method based on biological visual models