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Extended Min-Hash Focusing on Intersection Cardinality

机译:扩展的最小哈希专注于路口基数

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摘要

Min-Hash is a reputable hashing technique which realizes set similarity search. Min-Hash assumes the Jaccard similarity |A∩B|/|A∪B| as the similarity measure between two sets A and B. Accordingly, Min-Hash is not optimal for applications which would like to measure the set similarity with the intersection cardinality |A∩B|, since the Jaccard similarity decreases irrespective of |A∩B|, 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.
机译:Min-Hash是一种著名的哈希技术,可实现集合相似性搜索。 Min-Hash假定Jaccard相似度|A∩B| / |A∪B|因此,对于希望使用交集基数|A∩B|测量集合相似度的应用,Min-Hash并不是最佳选择,因为Jaccard相似度降低了,而与|A∩B无关|,作为| A |之间的差距和| B |变得更大。本文表明,通过稍微修改Min-Hash,我们可以有效解决Min-Hash固有的上述困难。理论分析和实验均表明我们的方法是有效的。

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