Semi-supervised Affinity Propagation Clustering Algorithm Based on Fireworks Explosion Optimization
Wang Limin, Han Xuming, Qiang Ji · 2014
In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, a semi-supervised affinity propagation clustering algorithm based on fireworks explosion optimization (FEO-SAP) was proposed in this study. The algorithm adjusts the similarity matrix by utilizing the known pair wise constraints, and performs affinity propagation on this basis. The idea of fireworks explosion was introduced into the iteration process of the algorithm. By adaptively searching the preference space bi-directionally, the algorithm's global and local searching abilities are balanced in order to find the optimal clustering structure. The results of the simulation experiments validated that the proposed algorithm has better clustering performance comparing with conventional AP and semi-supervised AP (SAP).