Find similar documents quickly
npm install minhash``
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Minhashing is an efficient similarity estimation technique that is often used to identify near-duplicate documents in large text collections. This package offers a JavaScript implementation of the minhash algorithm and an efficient Locality Sensitive Hashing Index for finding similar minhashes in Node.js or web applications.
To get started with Minhash.js, you can install the package with npm:
`bash`
npm install minhash --save
If you prefer, you can instead load the package directly in a browser:
`html`
#### Minhash Usage
Minhashes are hash representations of the contents within a set. The following example minhashes and then estimates the Jaccard similarity between two sets:
`javascript
import { Minhash } from 'minhash'; // If using Node.js
var s1 = ['minhash', 'is', 'a', 'probabilistic', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'datasets'];
var s2 = ['minhash', 'is', 'a', 'probability', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'documents'];
// create a hash for each set of words to compare
var m1 = new Minhash();
var m2 = new Minhash();
// update each hash
s1.map(function(w) { m1.update(w) });
s2.map(function(w) { m2.update(w) });
// estimate the jaccard similarity between two minhashes
m1.jaccard(m2);
`
#### LshIndex Usage
While one can compare the Jaccard similarity between a minhash and all others in a collection, the complexity of doing so is O(n), as one needs to compare the query set to every other set.
To estimate the results of the same comparison in sub-linear time, one can instead build a Locality Sensitive Hash Index, which maps hash sequences from a minhash signature to the list of document identifiers that contain the given hash sequence. Using this indexing technique, one can effectively find sets similar to a query set:
`javascript
import { Minhash, LshIndex } from 'minhash'; // If using Node.js
var s1 = ['minhash', 'is', 'a', 'probabilistic', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'datasets'];
var s2 = ['minhash', 'is', 'a', 'probability', 'data', 'structure', 'for',
'estimating', 'the', 'similarity', 'between', 'documents'];
var s3 = ['cats', 'are', 'tall', 'and', 'have', 'been',
'known', 'to', 'sing', 'quite', 'loudly'];
// generate a hash for each list of words
var m1 = new Minhash();
var m2 = new Minhash();
var m3 = new Minhash();
// update each hash
s1.map(function(w) { m1.update(w) });
s2.map(function(w) { m2.update(w) });
s3.map(function(w) { m3.update(w) });
// add each document to a Locality Sensitive Hashing index
var index = new LshIndex();
index.insert('m1', m1);
index.insert('m2', m2);
index.insert('m3', m3);
// query for documents that appear similar to a query document
var matches = index.query(m1);
console.log('Jaccard similarity >= 0.5 to m1:', matches);
`
The sample application uses minhash.js to compute the similarity between several sample documents:
There is also a sample Node.js script that can be run with node examples/index.js.
To run the test suite — npm run test. npm run build`.
To compile and minify minhash.min.js —