Gravitational Clustering: A Novel Approach for Efficient Supervised Learning with Minimal Data

Leveraging Physics-Inspired Models for Few-Shot Learning Efficiency

Abstract

Traditional supervised learning algorithms, such as neural networks and support vector machines, often struggle when training data is limited or when dealing with multiclass classification tasks. In response to these challenges, this paper introduces Gravitational Clustering, a novel algorithm that eliminates the need for predefined cluster numbers and effectively learns from small datasets. Drawing inspiration from gravitational physics, this method models each cluster as a planet with mass, radius, and class, allowing for dynamic cluster formation without the risk of overfitting. Key advantages include the ability to weight feature vectors, handle minimal data samples, and maintain resilience against overfitting. The algorithm demonstrates competitive performance across multiple datasets, achieving higher classification accuracy while maintaining lower computational complexity compared to traditional methods such as K-Means and support vector machines. This paper explores the algorithm’s theoretical foundations, computational efficiency, and empirical results, offering a robust solution for classification tasks with limited data availability.