Self-adaptive spectral cluster number detecting with particle swarm optimization algorithm
Chupeng Zeng, Aimin Zhou, Guixu Zhang · 2016
Spectral clustering algorithms have been playing an important role in solving many problems in pattern recognition and image processing. As a well-known spectral clustering algorithm, Normalized Cut has been proved powerful in image segmentation and data clustering. Morever spectral clustering has shown to be more effective in finding clusters than many traditional algorithms such as k-means. However, how to decide the number of clusters is always a crucial problem we confront. It's just yet acknownledge that evolutionary algorithms have a powerful ability to solve such optimization problems. In this paper, we apply a Validity Measure for Fuzzy Clustering(VMFC) to determine the cluster number in spectral clustering with the Particle Swarm Optimization selecting the optimal number of clusters from several possible choices.