Levenshtein distance is a measure of the difference between two strings. It is defined as the minimum number of operations (insertions, deletions, or substitutions) required to transform one string into the other.
Levenshtein distance is useful in a variety of applications, such as spell-checking, DNA sequence analysis, and fuzzy string matching. For example, in spell-checking, Levenshtein distance can be used to determine the closest spelling match for a misspelled word, by comparing the misspelled word to all the correctly spelled words in a dictionary and selecting the one with the smallest Levenshtein distance.
In addition, Levenshtein distance can be used as a way of quantifying the similarity between two strings, which can be useful in information retrieval, text mining, and data cleaning applications. For instance, it can be used to identify duplicate records in a database, even if the records have small differences in the spelling of names or addresses.
In summary, the Levenshtein distance is a useful tool for comparing and quantifying the difference between two strings and has many applications in a variety of domains.
Suppose you have two strings: “kitten” and “sitting”. The Levenshtein distance between these two strings would be 3, as the minimum number of operations to transform “kitten” into “sitting” would be to change “k” to “s”, “e” to “i”, and “n” to “g”.
Here’s a Python code sample to calculate the Levenshtein distance between two strings: