Y染色体短串联重复序列(YSTR)数据基于中心的软硬聚类建模

Ali Seman, Z. Bakar, Azizian Mohd Sapawi
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引用次数: 2

摘要

本文建立模型:(1)Y-STR数据和;(2) Y-STR软硬聚类。将Y-STR模型扩展并发展到Y-STR单倍群和Y-STR姓氏三个数据集上。结果表明,硬聚类模型和软聚类模型各有优缺点。软k-Means模型对Y-STR单倍群数据的聚类准确率为99.62%,硬k-Medoids模型对Y-STR姓数据的聚类准确率最高,为99.90%。在这种情况下,似乎两种模型都有同样的机会提高Y-STR聚类性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling centre-based hard and soft clustering for Y chromosome short tandem repeats (YSTR) data
This paper models: (1) Y-STR data and; (2) Y-STR hard and soft clustering. The Y-STR models are extended and developed to test on three data sets of Y-STR haplogroup and Y-STR Surname. The results show that the hard clustering models and the soft clustering models have their advantages and disadvantages. The soft k-Means model produces a good clustering accuracy of 99.62% for Y-STR haplogroup data, whereas the hard k-Medoids obtains the highest score of clustering accuracy of 99.90% for Y-STR Surname data. This scenario seems to be both models have an equally chance of improving Y-STR clustering performances.
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