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Article

Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors

1
Department of Inorganic Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1164, Bulgaria
2
Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, 400084 Cluj-Napoca, Romania
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Department of Analytical Chemistry, Chemical Faculty, Gdańsk University of Technology (GUT), 11/12 G. Narutowicza St., 80-233 Gdańsk, Poland
4
Department of Analytical Chemistry, Faculty of Chemistry and Pharmacy, University of Sofia, 1 James Bourchier Blvd., Sofia 1164, Bulgaria
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(11), 1763; https://doi.org/10.3390/sym12111763
Received: 5 September 2020 / Revised: 30 September 2020 / Accepted: 22 October 2020 / Published: 24 October 2020
(This article belongs to the Special Issue Chemometrics in Assessing Molecular Structures and Properties)
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. Additionally, the partitioning performed could be interpreted with respect to the molecular symmetry. The chemometric approach used for this goal is fuzzy c-means method being a semi-supervised clustering procedure. The advantage of such a clustering process is the opportunity to achieve separation of the solvents into similarity patterns with a certain degree of membership of each solvent to a certain pattern, as well as to consider possible membership of the same object (solvent) in another cluster. Partitioning based on a hybrid approach of the theoretical molecular descriptors and experimentally obtained ones permits a more straightforward separation into groups of similarity and acceptable interpretation. It was shown that an important link between objects’ groups of similarity and similarity groups of variables is achieved. Ten classes of solvents are interpreted depending on their specific descriptors, as one of the classes includes a single object and could be interpreted as an outlier. Setting the results of this research into broader perspective, it has been shown that the fuzzy clustering approach provides a useful tool for partitioning by the variables related to the main physicochemical properties of the solvents. It gets possible to offer a simple guide for solvents recognition based on theoretically calculated or experimentally found descriptors related to the physicochemical properties of the solvents. View Full-Text
Keywords: solvents; fuzzy hierarchical clustering; fuzzy associative-clustering; physicochemical descriptors solvents; fuzzy hierarchical clustering; fuzzy associative-clustering; physicochemical descriptors
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MDPI and ACS Style

Nedyalkova, M.; Sarbu, C.; Tobiszewski, M.; Simeonov, V. Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors. Symmetry 2020, 12, 1763. https://doi.org/10.3390/sym12111763

AMA Style

Nedyalkova M, Sarbu C, Tobiszewski M, Simeonov V. Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors. Symmetry. 2020; 12(11):1763. https://doi.org/10.3390/sym12111763

Chicago/Turabian Style

Nedyalkova, Miroslava, Costel Sarbu, Marek Tobiszewski, and Vasil Simeonov. 2020. "Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors" Symmetry 12, no. 11: 1763. https://doi.org/10.3390/sym12111763

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