Extended Min-Hash Focusing on Intersection Cardinality
Extended Min-Hash Focusing on Intersection Cardinality
复制标题
关注交叉点基数的扩展最小哈希
DOI：
10.1007/978-3-030-03493-1_3
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Takahisa Toda
中科院分区：
文献类型：
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作者：
Hisashi Koga;Satoshi Suzuki;Taiki Itabashi;Gibran Fuentes Pineda;Takahisa Toda
Min-Hash是一种著名的散列技术，它实现了集合相似性搜索。Min-Hash假设Jaccard相似性作为两个集合A和B之间的相似性度量。因此，Min-Hash对于想要用交集基数来测量集合相似性的应用不是最佳的，因为Jaccard相似性随着集合之间的差距而降低，|一|和|B|变得更大。通过对Min-Hash算法的改进，可以有效地解决Min-Hash算法的上述问题。理论分析和实验结果都表明了该方法的有效性。
Min-Hash is a reputable hashing technique which realizes set similarity search. Min-Hash assumes the Jaccard similarityas the similarity measure between two setsAandB. Accordingly, Min-Hash is not optimal for applications which would like to measure the set similarity with the intersection cardinality, since the Jaccard similarity decreases irrespective of, as the gap between |A| and |B| becomes larger. This paper shows that, by modifying Min-Hash slightly, we can effectively settle the above difficulty inherent to Min-Hash. Our method is shown to be valid both by theoretical analysis and with experiments.