y -短串联重复序列(Y-STR)数据聚类的硬、软更新质心

Ali Seman, Z. Bakar, Noorizam Daud
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引用次数: 4

摘要

比较了Y-STR数据聚类的硬更新质心和软更新质心。硬质心由New Fuzzy k-Modes聚类算法表示,软质心由k-Population算法表示。这两种算法通过Y-STR单倍群和Y-STR姓氏两个数据集进行了实验。结果表明,对于Y-STR数据,软质心性能优于硬质心。与新的模糊k-Modes算法的84.3%相比,软质心产生的平均聚类精度为86.3%。但总体结果表明,Y-STR数据聚类时,硬更新聚类优于软更新聚类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hard and soft updating centroids for clustering Y-short tandem repeats (Y-STR) data
This paper compares hard and soft updating centroids for clustering Y-STR data. The hard centroids represented by New Fuzzy k-Modes clustering algorithm, whereas the soft centroids represented through k-Population algorithm. These two algorithms are experimented through two datasets, Y-STR haplogroups and Y-STR Surnames. The results show that the soft centroid performance is better than the hard centroid for Y-STR data. The soft centroid produces 86.3% of the average clustering accuracy as compared 84.3% of the new fuzzy k-Modes algorithm. However, the overall result shows that the hard updating clustering is better than the soft updating clustering while clustering Y-STR data.
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