Text Clustering with Enhanced Novel Fast Seeds Affinity Propagation: Seed-Based Optimization

Text clustering is an essential technique in text mining and natural language processing for organizing large volumes of unstructured data into meaningful groups. Traditional clustering algorithms often face challenges in scalability, efficiency, and accuracy when dealing with high-dimensional text data. This work proposes an enhanced approach using Novel Fast Seeds Affinity Propagation (NFAP) to improve clustering performance. By introducing optimized seed selection, the method accelerates convergence, reduces computational complexity, and enhances the quality of cluster formation. Experimental results demonstrate that the proposed model achieves superior clustering accuracy and efficiency compared to standard affinity propagation methods, making it suitable for large-scale text mining applications.