一种基于荧光信息导航的聚类算法OACSTPCD

Clustering is a branch of unsupervised machine learning algorithms that has widespread applications in the informa-tion age.However,diverse research on clustering algorithms often faces issues such as it need to specify a fixed number of neighbors for density calculation,the requirement of predefining the number of clusters,and the necessity for multiple iterations to update information aggregation.These problems can lead to the loss of data features and increase computational complexity,resulting in higher time complexity of the models.To address these challenges,this paper inspired by the luminescence and light information transmission of fireflies and proposed a clustering algorithm called FLINCA.FLINCA consisted of two main modules,such as growing fireflies and merging fireflies trees.Firstly,it treated data points as fireflies,and determined their brightness u-sing an adaptive number of neighbors to achieve preliminary clustering.Then,it performed cluster fusion based on the firefly trees,resulting in the final clustering outcome.Experimental results demonstrate that FLINCA exhibits favorable clustering per-formance on four benchmark clustering datasets and three real-world multidimensional datasets compared to twelve different algo-rithms.This confirms the extensive applicability of FLINCA,which is based on firefly luminescence and light information trans-mission,in addressing the limitations of traditional clustering algorithms and improving clustering accuracy.