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Hard and soft updating centroids for clustering Y-short tandem repeats (Y-STR) data

机译:硬更新和软更新质心,用于聚类Y-短串联重复(Y-STR)数据

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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.
机译:本文比较了用于对Y-STR数据进行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,而软质心由k-Population算法表示。这两个算法是通过两个数据集(Y-STR单元组和Y-STR姓氏)进行实验的。结果表明,对于Y-STR数据,软质心的性能优于硬质心。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%。但是,总体结果表明,在对Y-STR数据进行聚类时,硬更新聚类优于软更新聚类。

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