An Improved Ant Colony Optimization Cluster Algorithm Based on Swarm Intelligence

This paper proposes an improved ant colony optimization cluster algorithm based on a classics algorithm LF algorithm. By the introduction of a new formula and the probability of similarity metric conversion function, as well as the new formula of distance, this algorithm can deal with the category data easily. It also introduces a new adjustment process, which adjusts the cluster generated by the carry process iteratively. We approve that that the algorithm can improve the efficiency and the convergence of the cluster theoretically. Data experiments show that the improved ant colony algorithm can form more accurate and stability clusters than the K-Modes algorithm, Information Entropy-Based Cluster Algorithm, and LF Algorithm. Scalability experiments show that the running time has an obvious linear relationship with the size of data set. Furthermore, we describe the process and idea of the algorithm usage by a mobile customer classification case and analyze the cluster results. This algorithm can handle large category dataset more rapidly, accurately and effectively, and keep the good scalability at the same time.