Application of computing in recognition of input design factors for vapour-grown carbon nanofibers through fuzzy cluster analysis.

The present investigation employed information mining and knowledge learning processes to showcase their efficacy in comprehending the viscoelastic properties of nanocomposites comprising vapor-grown carbon nanofiber and vinyl ester. The study relied solely on the data obtained from an experimental analysis. This study involves an investigation into the utilization of enhanced distance strategy in conjunction with the data-space clustering techniques possibilistic C-means and fuzzy possibilistic C-means. This study employs clustering methodologies to discern patterns of behaviour in the viscoelastic properties of polymer nano-composites. Principal component analysis is utilized as a dimensionality reduction technique to facilitate this analysis. By employing these methodologies, it was feasible to categories the nanocomposite specimens based on diverse attributes and partition the vapour-grown carbon nanofiber and vinyl ester specimens into distinct clusters. This paper emphasizes the significance and utility of data mining methodologies within the realm of materials informatics. [ABSTRACT FROM AUTHOR]