Related Experiment Video
Updated: Mar 17, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
Published on: April 8, 2020
Information Theory and Voting Based Consensus Clustering for Combining Multiple Clusterings of Chemical Structures
Faisal Saeed1,2, Naomie Salim3, Ammar Abdo4,5
1Faculty of Computing, Universiti Teknologi Malaysia, Malaysia. alsamet.faisal@gmail.com.
Abstract:
Many consensus clustering methods have been applied in different areas such as pattern recognition, machine learning, information theory and bioinformatics. However, few methods have been used for chemical compounds clustering. In this paper, an information theory and voting based algorithm (Adaptive Cumulative Voting-based Aggregation Algorithm A-CVAA) was examined for combining multiple clusterings of chemical structures. The effectiveness of clusterings was evaluated based on the ability of the clustering method to separate active from inactive molecules in each cluster, and the results were compared with Ward's method. The chemical dataset MDL Drug Data Report (MDDR) and the Maximum Unbiased Validation (MUV) dataset were used. Experiments suggest that the adaptive cumulative voting-based consensus method can improve the effectiveness of combining multiple clusterings of chemical structures.
Related Concept Videos
Molecular Models
Predicting Molecular Geometry
Resonance and Hybrid Structures
Resonance Structures and Resonance Hybrids
The Lewis structure of a nitrite anion (NO2−) may actually be drawn in two different ways, distinguished by the locations of the N–O and N=O bonds.
Molecules with Multiple Chiral Centers
MO Theory and Covalent Bonding
Inductive Effects on Chemical Shift: Overview

