Abstract
Density peak clustering (DPC) algorithm had superiority of clustering by finding the density peaks and fast search. In DPC algorithm, the cut-off distance had to be set by experience or at random, which would influence the clustering outcome. To find the best solution in domain of cut-off distance, this study proposed knowledge learning-based fruit fly optimization algorithm (KLFOA) to optimize the unknown parameters of the DPC model. The KLFOA had introduced the knowledge learning mechanism to enhance the searching abilities of fruit flies. In order to testify the precision and convergence of KLFOA, there were eight benchmark functions were involved in this paper and KLFOA had proved to have better performance than other compared swarm intelligence algorithms. The DPC based on KLFOA was utilized to identify eight clustering test data-sets. The simulation results showed that the KLFOA based DPC algorithm had better clustering quality and accuracy results.
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