Influence of the Number of Neighbours on the Clustering Metric by Oscillatory Chaotic Neural Network with Dipole Synaptic Connections
Vasyl Lytvyn, Dmytro Dudyk, Іван Пелещак, Роман Пелещак, Petro Ya. Pukach · 2024
Clustering is indispensable for addressing practical challenges across diverse domains in today's datadriven environment.Given the pivotal role of technology in managing vast amounts of data, effective data grouping has become indispensable for successful operations across various domains.For instance, in marketing, clustering aids in identifying customer segments for personalized marketing, while in medicine, it supports accurate diagnosis and treatment.Similarly, in financial analysis, it is vital for detecting anomalies or fraud, and in organizing textual data, it helps uncover fundamental trends.The emergence of oscillatory chaotic neural networks with dipole interactions offers a promising novel approach to clustering, leveraging self-organizing properties to group data effectively.Understanding how the number of nearest neighbours influences clustering metrics in this method is crucial for optimizing its efficiency and applicability.The study aims to calculate and analyse the evaluation of clustering metric values, including the Adjusted Rand Index (ARI) and silhouette coefficient (SC), concerning the number of nearest neighbours and clustering resolution to determine the optimal number of nearest neighbours for enhancing clustering quality.Oscillatory chaotic neural networks with dipole synaptic connections between neurons were employed.To ensure a comprehensive analysis, four diverse datasets were utilized, each chosen for its distinct characteristics, representing different complexities commonly encountered in real-world data scenarios: Atom (linear inseparability), WingNut (small inter-cluster/large intra-cluster distances), TwoDiamonds (weak link connecting clusters), and EngyTime (overlapping clusters of different densities).Clustering was performed across different ranges of nearest neighbour values (Atom: 1-300, WingNut: 1-800, TwoDiamonds: 1-400, EngyTime: 1-1000) and resolution levels to comprehensively assess the influence of nearest neighbour selection on clustering quality across various data complexities.The study revealed a significant impact of the number of nearest neighbours on clustering efficiency when employing oscillatory chaotic neural networks.Networks with dipole synaptic connections exhibited less sensitivity to changes in the number of nearest neighbours compared to those with Gaussian-based synaptic connections, indicating their robustness.Additionally, the optimal number of nearest neighbours varied across datasets and resolution levels, highlighting the need for tailored parameter selection to maximize clustering quality.The results confirm the importance of selecting the optimal number of nearest neighbours to enhance clustering quality using an oscillatory chaotic neural network.Further research could explore additional factors influencing clustering performance.