一种基于荧光信息导航的聚类算法.

Clustering is a branch of unsupervised machine learning algorithms that has widespread applications in the information age. However, diverse research on clustering algorithms often faces issues such as the 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 study was inspired by the luminescence and light information transmission of fireflies and proposed a clustering algorithm called FLINCA（Firefly Luminescent Information Navigation Clustering Algorithm). FLINCA consists of two main modules: "Growing Fireflies" and "Merging Fireflies Trees. " Firstly, data points are treated as fireflies, and their brightness is determined using an adaptive number of neighbors to achieve preliminary clustering. Then, cluster fusion is performed based on the firefly trees, resulting in the final clustering outcome. Experimental results demonstrate that FLINCA exhibits favorable clustering performance on four benchmark clustering datasets and three real-world multidimensional datasets compared to twelve different algorithms. This confirms the extensive applicability of FLINCA, which is based on firefly luminescence and light information transmission, in addressing the limitations of traditional clustering algorithms and improving clustering accuracy. [ABSTRACT FROM AUTHOR]