winkjs / wink-distance

Distance/Similarity functions for Bag of Words, Strings, Vectors and more.

wink-distance

Distance/Similarity functions for Bag of Words, Strings, Vectors and more.

Compute distances or similarities needed for NLP, de-duplication and clustering using wink-distance. Some of the methods are listed below:

1. Cosine similarity for Bag of Words,
2. Jaccard & Tversky for Sets,
3. Jaro, Jaro-Winkler, and Levenshtien for string,
4. Chebyshev and Taxicab for vectors.

Combine wink-distance with WinkNLP to build awesome NLP apps.

Installation

Use npm to install:

npm install wink-distance --save

Documentation

Check out the distance/similarity API documentation to learn more.

Need Help?

If you spot a bug and the same has not yet been reported, raise a new issue or consider fixing it and sending a pull request.

About wink

Wink is a family of open source packages for Statistical Analysis, Natural Language Processing and Machine Learning in NodeJS. The code is thoroughly documented for easy human comprehension and has a test coverage of ~100% for reliability to build production grade solutions.

Copyright & License

wink-distance is copyright 2017-23 GRAYPE Systems Private Limited.

It is licensed under the terms of the MIT License.

wink-distance's Issues

update dependencies

update copyright notice year in README & License Header

fix soundex to return distance instead of similarity

update wink's link to point to winkjs.org

Add dependencies shield from david-dm

Alternate Distance Forms related to Pair Counting

With reference to GiulioRossetti/cdlib#147 (comment)
There are other distance algorithms that can be used based on pair counting metrics.
The methods are shown in https://www.semanticscholar.org/paper/Comparison-of-Jaccard%2C-Dice%2C-Cosine-Similarity-To-Thada-Jaglan/8575e8beef47bd2880c92f54a749f933db983e56 For Dice, Cosine, and Jaccard.

• Generalized Pair-Counting Similarity Measures for Clustering and Cluster Ensembles
• Ground truth bias in external cluster validity indices
• Systematic Analysis of Cluster Similarity Indices: Towards Bias-free Cluster Validation