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距离度量 —— 杰卡德距离(Jaccard Distance)
2022-07-02 17:08:00 【繁依Fanyi】
Python学习系列文章:目录

一、概述
杰卡德距离(Jaccard Distance),是用来衡量两个集合差异性的一种指标,它是杰卡德相似系数的补集。
二、计算公式
① 杰卡德相似系数
杰卡德相似系数(Jaccard similarity coefficient):两个集合 A 和 B 的交集元素在 A,B 的并集中所占的比例,称为两个集合的杰卡德相似系数,用符号 J ( A , B ) J(A,B) J(A,B) 表示,则其表达式为: J ( A , B ) = ∣ A ∩ B ∣ ∣ A ∪ B ∣ J(A,B)=\frac{|A\cap B|}{|A\cup B| } J(A,B)=∣A∪B∣∣A∩B∣
② 杰卡德距离
杰卡德距离(Jaccard Distance):与杰卡德相似系数相反,用两个集合中不同元素占所有元素的比例来衡量两个集合的区分度。杰卡德距离的表达式为: J δ ( A , B ) = 1 − J ( A , B ) = ∣ A ∪ B ∣ − ∣ A ∩ B ∣ ∣ A ∪ B ∣ J_{\delta}(A,B)=1-J(A,B)=\frac{|A\cup B|-|A\cap B|}{|A\cup B|} Jδ(A,B)=1−J(A,B)=∣A∪B∣∣A∪B∣−∣A∩B∣

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