RBF + K-Means Clustering: Integration and Applications

This article explores the application of RBF combined with K-means clustering. We begin by reviewing the fundamental concepts: Radial Basis Function (RBF) networks, an artificial neural network architecture commonly employed for classification and regression tasks, and K-means clustering, an unsupervised algorithm designed to partition data into distinct groups. When integrated, the RBF+K-means algorithm leverages K-means to determine optimal centers for the RBF's hidden layer neurons, typically using the cluster centroids obtained from K-means as the RBF centers. This hybrid approach enhances performance in both classification and clustering tasks by enabling more efficient handling of large datasets and providing deeper insights into data structure and feature characteristics. The implementation typically involves first applying K-means to identify cluster centers, then using these centers to initialize the RBF network's Gaussian functions, often with widths calculated based on cluster variances. For future research directions, we propose extending the RBF+K-means algorithm to broader domains such as image processing, where it could optimize feature extraction, and natural language processing, potentially improving text categorization through enhanced cluster-based feature representation.