چکیده مقاله
This study presents a novel application of unsupervised machine learning techniques to analyze the molecular and evaporative characteristics of perfumery compounds A dataset comprising molecular descriptors, structural notations, and physical properties of scent compounds has been prepared using three extensive SQL databases, and some well known methodological approaches including Principal Component Analysis PCA and Factor Analysis FA for dimensionality reduction and Hierarchical Clustering HC are implemented to identify intrinsic olfactory families without relying on pre existing classes
کلیدواژهها
نویسندگان
T Manouchehri
Department of Statistics, Shiraz University, Shiraz, Iran
شیوه ارجاع
Manouchehri, T,1403,From Molecules to Clusters: Unsupervised Learning Insights into Perfume Composition,1st International Biennial Conference of Artificial Intelligence and Data Science 2024,Bushehr
ارائهشده در
مجموعه مقالات اولین کنفرانس بین المللی دوسالانه هوش مصنوعی و علوم داده3 اردیبهشت 1403 · بوشهر