School of Management Science and Engineering, Shandong Normal University, Jinan 250014, China.
Comput Intell Neurosci. 2017;2017:4367342. doi: 10.1155/2017/4367342. Epub 2017 Nov 16.
Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.
集成聚类可以通过整合多个基础聚类来提高单个聚类算法的泛化能力，并生成更稳健的聚类结果，因此成为当前聚类研究的焦点。集成聚类旨在找到一个尽可能与基础聚类一致的共识分区。遗传算法是一种从生物学的自然选择和进化机制中发展而来的高度并行、随机和自适应搜索算法。在本文中，通过改进染色体的编码，设计了一种改进的遗传算法。通过使用遗传机制作为进化规则并结合类似于细胞的 P 系统的通信机制，构建了一种新的膜进化算法。所提出的算法用于优化基础聚类，并找到最佳染色体作为最终的集成聚类结果。遗传算法的全局优化能力和膜系统的快速收敛使得膜进化算法在六个真实 UCI 数据集上的表现优于几种最先进的技术。
