Improved Fruit Fly Optimization Algorithm-based Density Peak Clustering and Its Applications

Original scientific paper As density-based algorithm, Density Peak Clustering (DPC) algorithm has superiority of clustering by finding the density peaks. But the cut-off distance and clustering centres had to be set at random, which would influence clustering outcomes. Fruit flies find the best food by local searching and global searching. The food found was the parameter extreme value calculated by Fruit Fly Optimization Algorithm (FOA). Based on the rapid search and fast convergence superiorities of FOA, it is possible to make up the casualness of DPC. An improved fruit fly optimization-based density peak clustering algorithm was proposed as FOA-DPC. The FOA-DPC algorithm would be more efficient and effective than DPC algorithm. The results of seven simulation experiments in UCI data sets validated that the proposed algorithm did not only have better clustering performance, but also were closer to the true clustering numbers. Furthermore, FOA-DPC was applied to practical financial data analysis and the conclusion was also effective.