How can the Levenshtein algorithm be used to calculate edit distance and determine the similarity between two strings in Java?-javaTutorial-php.cn

How can the Levenshtein algorithm be used to calculate edit distance and determine the similarity between two strings in Java?

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Release： 2024-11-18 06:28:02

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How can the Levenshtein algorithm be used to calculate edit distance and determine the similarity between two strings in Java?

Similarity String Comparison in Java

When comparing multiple strings to identify the most similar ones, it is essential to leverage appropriate techniques and algorithms. This article delves into a widely-used approach known as "edit distance" to calculate the similarity between two strings.

Calculating Edit Distance using the Levenshtein Algorithm

Calculating edit distance involves determining the minimum number of character insertions, deletions, and substitutions required to transform one string into another. The Levenshtein algorithm is a classic approach for computing edit distance, often incorporated into programming libraries. To calculate using the Levenshtein algorithm:

// Levenshtein's Edit Distance Function
public static int editDistance(String s1, String s2) {
    // Convert to lower case for case-insensitive comparison
    s1 = s1.toLowerCase();
    s2 = s2.toLowerCase();

    int[][] matrix = new int[s2.length() + 1][s1.length() + 1];

    // Initialize first column to cost of insertion
    for (int i = 0; i <= s1.length(); i++) {
        matrix[0][i] = i;
    }

    // Initialize first row to cost of deletion
    for (int j = 0; j <= s2.length(); j++) {
        matrix[j][0] = j;
    }

    // Populate the matrix
    for (int j = 1; j <= s2.length(); j++) {
        for (int i = 1; i <= s1.length(); i++) {
            int cost = s1.charAt(i - 1) == s2.charAt(j - 1) ? 0 : 1;
            int min = Math.min(matrix[j - 1][i] + 1, // Deletion
                    Math.min(matrix[j][i - 1] + 1, // Insertion
                            matrix[j - 1][i - 1] + cost)); // Substitution
            matrix[j][i] = min;
        }
    }

    return matrix[s2.length()][s1.length()];
}

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Normalized Similarity Index

Once the edit distance is calculated, the similarity index can be computed by normalizing it to the length of the longer string:

// Similarity Index Function
public static double similarityIndex(String s1, String s2) {
    int distance = editDistance(s1, s2);
    String longer = s1.length() > s2.length() ? s1 : s2;
    double similarity = 1.0 - (distance / (double) longer.length());
    return similarity;
}

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Usage Example:

To utilize these methods, you can apply them as follows:

String str1 = "The quick fox jumped";
String str2 = "The fox";
double similarity = similarityIndex(str1, str2);
System.out.println("Similarity Index: " + similarity);

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Output:

Similarity Index: 0.70

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This example demonstrates a similarity index of 0.7 between "The quick fox jumped" and "The fox".

Overall, the techniques described in this article provide a robust way to quantify string similarity, allowing for efficient and effective comparison of multiple strings.

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