Decision Theoretic Rough Set-Based Neighborhood for Self-Organizing Map

Autor:	Sresht Agrawal, Sudip Ghosh, Shubhra Sankar Ray
Rok vydání:	2021
Předmět:	Self-organizing map symbols.namesake Computer science Gaussian function symbols Process (computing) Dunn index Rough set Cluster analysis Algorithm Selection (genetic algorithm) Associative property
Zdroj:	SN Computer Science. 2
ISSN:	2661-8907 2662-995X
DOI:	10.1007/s42979-021-00490-2
Popis:	A decision theoretic rough set-based neighborhood selection process is developed for self-organizing maps. While the neighborhood of the winner neuron is selected based on the probability of its associativity to the winner neuron, the selected neighborhood is updated using a new method which combines the probability of its associativity and the Gaussian function. This approach provides better results as compared to self-organizing map and other clustering algorithms on several real-life datasets. The results are evaluated in terms of DB index, Dunn index, quantization error, ARI, and NMI.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::928156a224a49c3f9deb5035b4ba7257 https://doi.org/10.1007/s42979-021-00490-2 Zobrazit plný text záznamu Full text from SpringerLink