Solved – Jaccard distance vs Levenshtein distance for fuzzy matching

Question

My data is similar to the following data, but far bigger and more complex.

Apple
Banana
Those fruits
Tomato 
Cocumber
These vegetables

I would like to get the following result:

Those fruits
These vegetables

Using the agrep/agrepl function in R I received a first result. However agrep and agrepl use the Levenshtein distance as default. An alternative would be the Jaccard distance.

Jaccard distance vs Levenshtein distance: Which distance is better for fuzzy matching?

There is already a similar question:
Properties of Levenshtein, N-Gram, cosine and Jaccard distance coefficients – in sentence matching. However I would like to know which distance works best for Fuzzy matching.

Extra credits: Are other distance measure (e.g. N-Gram, Cosine, Geometric, Manhattan) also useful for Fuzzy matching? Implementations in R are also welcome.

Answer 1

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You can use Naive Bayes algorithm:

Naive Bayes - Wikipedia