二部图
数学
匹配(统计)
组合数学
相似性(几何)
系统发育树
公制(单位)
度量空间
树(集合论)
星团(航天器)
图形
离散数学
统计
人工智能
计算机科学
图像(数学)
运营管理
经济
化学
基因
生物化学
程序设计语言
作者
Damian Bogdanowicz,Krzysztof Giaro
标识
DOI:10.2478/amcs-2013-0050
摘要
Abstract The Robinson-Foulds (RF) distance is the most popular method of evaluating the dissimilarity between phylogenetic trees. In this paper, we define and explore in detail properties of the Matching Cluster (MC) distance, which can be regarded as a refinement of the RF metric for rooted trees. Similarly to RF, MC operates on clusters of compared trees, but the distance evaluation is more complex. Using the graph theoretic approach based on a minimum-weight perfect matching in bipartite graphs, the values of similarity between clusters are transformed to the final MC-score of the dissimilarity of trees. The analyzed properties give insight into the structure of the metric space generated by MC, its relations with the Matching Split (MS) distance of unrooted trees and asymptotic behavior of the expected distance between binary n-leaf trees selected uniformly in both MC and MS (Θ(n 3/2 )).
科研通智能强力驱动
Strongly Powered by AbleSci AI