计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 126-129.doi: 10.11896/jsjkx.190900113
魏霖静1, 宁璐璐2, 郭斌3, 侯振兴4, 甘诗润1
WEI Lin-jing1, NING Lu-lu2, GUO Bin3, HOU Zhen-xing4, GAN Shi-run1
摘要： 为了降低K-mediods聚类算法的误差并提高并行优化的性能,将混合蛙跳算法运用于聚类和并行优化过程。在K-mediods聚类过程中,将K-mediods与聚类簇思想相结合,对各个聚类簇进行混合蛙跳算法优化,从而提高了大规模数据样本聚类的效率。针对多个聚类执行节点并行完成大规模样本K-mediods聚类时,混合蛙跳算法有效提高了加速比。实验证明,相比于普通的K-mediods聚类,基于混合蛙跳算法的K-mediods聚类优势明显,且处理大规模样本的加速比性能更优。
中图分类号:
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