Comparison of hierarchical clustering methods for binary data from molecular markers

Q4 Mathematics
E. Pratsinakis, S. Ntoanidou, A. Polidoros, C. Dordas, P. Madesis, I. Eleftherohorinos, G. Menexes
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引用次数: 1

Abstract

Data from molecular markers used for constructing dendrograms, which are based on genetic distances between different plant species, are encoded as binary data. For dendrograms' construction, the most commonly used linkage method is the UPGMA in combination with the squared Euclidean distance. It seems that in this scientific field, this is the 'golden standard' clustering method. In this study, a review is presented on clustering methods used with binary data. Furthermore, an evaluation of the linkage methods and the corresponding appropriate distances (comparison of 163 clustering methods) is attempted using binary data resulted from molecular markers applied to five populations of the wild mustard Sinapis arvensis species. The validation of the various cluster solutions was tested using external criteria. The results showed that the 'golden standard' is not a 'panacea' for dendrogram construction, based on binary data derived from molecular markers. Thirty seven other hierarchical clustering methods could be used.
分子标记二值数据的层次聚类方法比较
用于构建树形图的分子标记数据基于不同植物物种之间的遗传距离,被编码为二进制数据。对于树形图的构建,最常用的联动方法是UPGMA结合欧氏距离的平方。在这个科学领域,这似乎是聚类方法的“黄金标准”。本文对二值数据的聚类方法进行了综述。在此基础上,对5个野生芥菜种群的分子标记进行了二元数据分析,并比较了163种聚类方法的适宜距离。使用外部标准测试了各种集群解决方案的有效性。结果表明,基于分子标记的二进制数据,“黄金标准”并不是树形图构建的“万灵药”。37种其他层次聚类方法可以被使用。
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来源期刊
International Journal of Data Analysis Techniques and Strategies
International Journal of Data Analysis Techniques and Strategies Decision Sciences-Information Systems and Management
CiteScore
1.20
自引率
0.00%
发文量
21
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