RBF + K-means Clustering Algorithm

Numerous clustering algorithms are available for data analysis, including K-means, hierarchical clustering, and DBSCAN. In neural networks, the Radial Basis Function (RBF) network also serves as a popular clustering method. This article explores a hybrid approach combining RBF networks with K-means algorithm to perform clustering analysis. The implementation typically involves first using K-means to determine cluster centers, which then serve as the basis functions for the RBF network. The RBF layer calculates Gaussian activations based on distance metrics from these centers, while the output layer performs linear combinations for final cluster assignments. This combined approach enables better pattern recognition in datasets and generates more accurate clustering results by leveraging both centroid-based partitioning and neural network adaptability. Key implementation steps include: 1) Initializing K-means to find cluster centroids 2) Setting RBF centers equal to K-means centroids 3) Calculating Gaussian widths using inter-center distances 4) Training the output layer weights through linear regression or gradient descent.