基于教与学优化改进的近邻传播聚类算法

针对近邻传播聚类(AP)中偏向参数和阻尼因子设定导致聚类效果有一定局限性的问题,提出了一种基于教与学优化算法(TLBO)的近邻传播聚类.首先确定偏向参数p的搜索空间,然后使用教与学优化算法在搜索空间中寻找最优参数值,同时在聚类过程中自适应调整阻尼因子防止发生震荡,从而提高AP算法的聚类质量.实验表明,该算法能有效的解决偏向参数和阻尼因子对聚类结果造成的局限性,提高了聚类的轮廓系数,并降低了聚类错误率.

Affinity Propagation Clustering Based on Teaching Learning-Based Optimization

Aiming at the limitation of the clustering effect caused by the preference and damping factors in Affinity Propagation (AP), a Teaching and Learning Based Optimization (TLBO) algorithm is proposed. First, the search space of parameter p is determined, and then the TLBO algorithm is used to find the optimal parameter value in the search space. At the same time, the damping factor is automatically adjusted to prevent numerical oscillations during the clustering process, so as to improve the clustering quality of AP algorithm. The experimental results show that the algorithm can effectively solve the problem caused by preference and damping factors, improve the contour coefficient of clustering, and reduce the clustering error rate.