S S symmetry Article Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors Miroslava Nedyalkova 1, Costel Sarbu 2 , Marek Tobiszewski 3 and Vasil Simeonov 4,* 1 Department of Inorganic Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1164, Bulgaria;
[email protected]fia.bg 2 Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, 400084 Cluj-Napoca, Romania;
[email protected] 3 Department of Analytical Chemistry, Chemical Faculty, Gda´nskUniversity of Technology (GUT), 11/12 G. Narutowicza St., 80-233 Gda´nsk,Poland;
[email protected] 4 Department of Analytical Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1164, Bulgaria * Correspondence:
[email protected]fia.bg Received: 5 September 2020; Accepted: 22 October 2020; Published: 24 October 2020 Abstract: The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of a large group of solvents using specific descriptors. The fuzzy divisive hierarchical associative-clustering algorithm provides not only a fuzzy partition of the solvents investigated, but also a fuzzy partition of descriptors considered. In this way, it is possible to identify the most specific descriptors (in terms of higher, smallest, or intermediate values) to each fuzzy partition (group) of solvents.