From Molecules to Clusters: Unsupervised Learning Insights into Perfume Composition

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.

کلیدواژه ها:

Perfumery ، Molecular Structure ، Machine Learning ، Factor Analysis ، Principal Component Analysis ، Hierarchical Clustering