Dynamic Clustering Algorithm Program Based on Sample and Principal Axis Kernel Function Similarity

This dynamic clustering algorithm program calculates similarities between input samples based on their features and principal axis kernel functions, then dynamically clusters them together. The core implementation involves computing kernel-based similarity matrices using radial basis functions or polynomial kernels, followed by iterative clustering updates that adjust cluster assignments based on real-time similarity measurements. Key algorithmic components include: - Feature normalization and dimensionality preprocessing - Kernel matrix computation using principal component analysis (PCA) for axis optimization - Dynamic threshold adjustment for cluster merging and splitting - Convergence checking through silhouette coefficient evaluation This algorithm provides valuable insights into data structures and patterns, with applications spanning data mining, pattern recognition, and machine learning domains. It effectively uncovers hidden patterns within large datasets and extracts actionable knowledge. The implementation can be efficiently developed using programming languages like Python (with scikit-learn for kernel operations), Java (utilizing matrix libraries for similarity calculations), or MATLAB (leveraging built-in kernel functions and clustering toolboxes). The program structure typically involves modular components for data input, kernel computation, clustering iteration, and result visualization. This algorithm program offers significant utility for research and practical applications requiring adaptive clustering solutions.