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https://issues.apache.org/jira/browse/LUCENE-6968?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15093091#comment-15093091
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Cao Manh Dat edited comment on LUCENE-6968 at 1/12/16 1:36 AM:
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Yes, It kinda like finding K nearest neighbor. But there a different here:
- In K nearest neighbor, we use K as parameter which decide how many nearest
neighbor we wanna retrieve.
- In LSH we use a radius as parameter. The radius define minimum distance
between center doc and other docs. (LSH is also far faster than K nearest
neighbor)
In both case, the result will be rank by distance to the center doc.
was (Author: caomanhdat):
Yes, It kinda like finding K nearest neighbor. But there a different here:
- In K nearest neighbor, we use K as parameter which decide how many nearest
neighbor we wanna retrieve.
- In LSH we use a radius as parameter. The radius define minimum distance
between center doc and other docs.
In both case, the result will be rank by distance to the center doc.
> LSH Filter
> ----------
>
> Key: LUCENE-6968
> URL: https://issues.apache.org/jira/browse/LUCENE-6968
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Cao Manh Dat
> Attachments: LUCENE-6968.patch, LUCENE-6968.patch
>
>
> I'm planning to implement LSH. Which support query like this
> {quote}
> Find similar documents that have 0.8 or higher similar score with a given
> document. Similarity measurement can be cosine, jaccard, euclid..
> {quote}
> For example. Given following corpus
> {quote}
> 1. Solr is an open source search engine based on Lucene
> 2. Solr is an open source enterprise search engine based on Lucene
> 3. Solr is an popular open source enterprise search engine based on Lucene
> 4. Apache Lucene is a high-performance, full-featured text search engine
> library written entirely in Java
> {quote}
> We wanna find documents that have 0.6 score in jaccard measurement with this
> doc
> {quote}
> Solr is an open source search engine
> {quote}
> It will return only docs 1,2 and 3 (MoreLikeThis will also return doc 4)
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