jaccard neo4j

Jaccard Similarity算法是一种用来计算样本集合之间相似度的算法。它通过计算两个集合的交集大小与并集大小的比值来衡量相似度。Jaccard系数越大，说明集合之间的相似度越大。在Neo4j中，可以使用`algo.similarity.jaccard`函数来计算Jaccard Similarity。该函数接受两个样本集合作为参数，并返回它们之间的相似度值。示例代码如下：

CALL algo.similarity.jaccard([1,2,3], [1,2,4,5]) AS similarity

这段代码计算了集合和集合之间的Jaccard相似度，并将结果存储在名为`similarity`的变量中。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [neo4j相似性算法（Similarity algorithms）-1.The Jaccard Similarity algorithm](https://blog.csdn.net/name__student/article/details/97010623)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [NEO4J-相似度算法01-杰卡德相似度介绍及应用场景简介](https://blog.csdn.net/lijunliang2017/article/details/119544863)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v92^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
[ .reference_list ]