计算机科学 ›› 2022, Vol. 49 ›› Issue (5): 165-169.doi: 10.11896/jsjkx.210800218
张宇姣1, 黄锐2, 张福泉2, 隋栋3, 张虎4
ZHANG Yu-jiao1, HUANG Rui2, ZHANG Fu-quan2, SUI Dong3, ZHANG Hu4
摘要： 为了提高近邻传播聚类算法的聚类性能,采用菌群算法进行近邻传播偏向参数优化求解。首先,根据待聚类样本建立相似矩阵,初始化偏向参数;然后采用菌群算法优化偏向参数,将偏向参数作为菌落进行训练,设置轮廓(Silhouette)指标值作为菌群算法的适应度函数;接着通过菌落位置更新优化后的偏向参数,进行近邻传播聚类运算,不断更新近邻传播聚类算法的决策和潜力阵;最后获得稳定的聚类结果。实验结果表明,合理设置菌群优化算法的参数,能够获得较好的聚类效果。在电商数据集和UCI数据集中,相比常用聚类算法,所提算法能够获得更高的Silhouette指标值和最短的欧氏距离,在聚类分析中的适用度较高。
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