A Dyeing Clustering Algorithm based on Ant Colony Path-finding Mechanism

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Abstract

Clustering has a wide range of applications in various industries. In different research ideas, bionic algorithms have heuristic help for clustering research. However, most of the clustering algorithms based on bionics often have problems of insufficient information extraction, too many required parameters, and limited application scope. Therefore, these algorithms have lower accuracy than traditional clustering algorithms. To solve these problems, this paper proposes a new ant colony clustering algorithm, which is called DCACP (Dyeing Clustering Algorithm based on Ant Colony Path-finding). This algorithm combines the idea of dyeing with pheromones for the first time and realizes clustering by using ants to crawl and dye each point. This can better capture data features, improve fault tolerance and achieve a better clustering effect. In addition, the algorithm is constructed as a dynamic system to deeply mine data information with the strategy of swarm intelligence, including the ant colony generation model, path-finding model, path-finding termination model, and dyeing clustering model. Besides, to evaluate the algorithm, we selected 2 simulation data sets, 5 artificial data sets, 5 real-world data sets, 9 comparison algorithms, and 8 evaluation indexes. From the comprehensive multi-round experiments, seven of the eight evaluation indexes of the DCACP algorithm are more advantageous than other comparison algorithms.
This paper is currently under review, but the relevant code can be found at GitHub.