What is Levenshtein Distance?

Introduction

As you work on a significant document, let’s say you see you’ve spelled a word incorrectly. It can be difficult to find and fix these kinds of mistakes by hand. Now for the intriguing Levenshtein Distance: it measures the amount of work needed to change one sequence into another, providing an effective tool for sequence comparison and error repair. This measure, named for the mathematician Vladimir Levenshtein, transforms how we approach jobs like DNA sequencing and spell checking. It is essential in the digital age, where accuracy and precision are crucial.

Learning Outcomes

• Explain what Levenshtein Distance is and why it matters.
• List and explain the steps needed to calculate the Levenshtein Distance.
• Determine Levenshtein the separation of two sequences by the use of dynamic programming.
• Apply the concept to practical problems like spell-checking and sequence comparison.
• Examine and evaluate the outcomes of Levenshtein Distance computations in practical situations.

What is Levenshtein Distance?

The Levenshtein Distance quantifies the degree of difference between two sequences. By counting the bare minimum of operations required to convert one sequence into another, it quantifies this difference. The following operations are permitted:

• Insertion: Adding a character to a sequence.
• Deletion: Removing a character from a sequence.
• Substitution: Replacing one character with another.

How It Works?

We employ a matrix-based dynamic programming method to determine the Levenshtein Distance between two strings. Here is a detailed procedure:

Matrix Initialization

• Make a matrix in which the first i characters of the first string. And then the first j characters of the second string are represented by the cell (i, j).
• Initialize the first row and the first column. The value at cell (i, 0) represents the distance between the first i characters of the first string and an empty second string (which is simply i). Similarly, (0, j) represents the distance between an empty first string and the first j characters of the second string.

Filling the Matrix

• For each cell (i, j), determine the cost of three operations:
  • Insertion: The value from cell (i, j-1) + 1
  • Deletion: The value from cell (i-1, j) + 1
  • Substitution: The value from cell (i-1, j-1) plus cost, where cost is 1 in the case of different characters and 0 in the case of identical characters.
• Select the lowest value from these three choices and allocate it to the corresponding cell (i, j).

Extracting the Result

• The value in the bottom-right cell of the matrix represents the Levenshtein Distance between the two strings.

Example

The Levenshtein Distance between the strings “kitten” and “sitting” will be calculated now.

Initialize Matrix

• Rows represent characters from “kitten”.
• Columns represent characters from “sitting”.
• First row and column are filled based on indices (representing insertion or deletion operations).

Fill Matrix

• Compare characters and fill each cell based on the minimum cost of insertion, deletion, or substitution.

Calculate Distance

• After filling in the matrix, the bottom-right cell provides the distance.

Detailed Step-by-Step Calculation

You start the matrix using the lengths of the two strings, “kitten” (6 characters) and “sitting” (7 characters). Then, you fill it using the insertion, deletion, and substitution methods.

Initialize the Matrix: The initial matrix with the first row and column filled with indices looks like this:

Fill the Matrix: Insertion, deletion, or substitution are the three operations that can be used to fill each cell (i, j). Let’s walk through each cell’s procedure one by one.

Comparing ‘k’ (kitten) with ‘s’ (sitting):

• Insert ‘k’: Cost = 2 (1 + 1)
• Delete ‘s’: Cost = 2 (1 + 1)
• Substitute ‘k’ with ‘s’: Cost = 1 (0 + 1)
• Minimum cost = 1 (substitute)

Continue filling the matrix in a similar manner for each character pair:

Final Matrix Explanation

• First row: Represents transforming “kitten” into an empty string through deletions.
• First column: Represents transforming an empty string into “sitting” through insertions.
• Matrix cells: Represent the cost of transforming prefixes of “kitten” to prefixes of “sitting”.

The bottom-right cell (7, 7) represents the Levenshtein distance of 3 between the entire “kitten” and “sitting”. This suggests that the transformation of “kitten” into “sitting” requires three operations (substitutions and insertions).

Conclusion

By counting the number of modifications needed to change one sequence into another, the Levenshtein Distance offers a useful metric for evaluating sequence similarity. It is a vital tool for sequence comparison and error correction, with applications ranging from genetic studies to spell checking. Comprehending and implementing this idea facilitates the resolution of practical issues where sequence transformation and similarity play crucial roles.

Frequently Asked Questions

Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.