Fuzzy Search

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Introduction

This article describes a simple implementation of the fuzzy (string) search. It can be used for approximate string matching (for more information, see http://en.wikipedia.org/wiki/Fuzzy_string_searching).

Other algorithms for approximate string searching exist (e.g., Soundex), but those aren't as easy to implement. The algorithm in this article is easy to implement, and can be used for tasks where approximate string searching is used in an easy way.

A List<string> is used for searching, and therefore it's quite easy to search a database.

Background

The algorithm used the Levenshtein-distance for determining how exact a string from a word list matches the word to be found. Information about the Levenshtein-distance can be found at http://en.wikipedia.org/wiki/Levenshtein_distance.

Using the code

The following example will show how simply the class can be used.

static void Main(string[] args)
{
    string word = "Code Project";
    List<string> wordList = new List<string>
    {
        "Code Project",
        "Code project",
        "codeproject",
        "Code Projekt",
        "Kode Project",
        "Other Project"
    };

    List<string> foundWords = FuzzySearch.Search(
        word,
        wordList,
        0.70);

    foundWords.ForEach(i => Console.WriteLine(i));
    Console.ReadKey();
}

Output:

Code Project
Code project
codeproject
Code Projekt
Kode Project

Implementation

A basic approach is shown. Instead of the Levenshtein-distance, a more optimized algorithm could be used - but here, a quite simple implementation is given for clarity reasons.

Levenshtein-distance

For computing the Levenshtein-distance, I use the following algorithm:

public static int LevenshteinDistance(string src, string dest)
{
    int[,] d = new int[src.Length + 1, dest.Length + 1];
    int i, j, cost;
    char[] str1 = src.ToCharArray();
    char[] str2 = dest.ToCharArray();

    for (i = 0; i <= str1.Length; i++)
    {
        d[i, 0] = i;
    }
    for (j = 0; j <= str2.Length; j++)
    {
        d[0, j] = j;
    }
    for (i = 1; i <= str1.Length; i++)
    {
        for (j = 1; j <= str2.Length; j++)
        {

            if (str1[i - 1] == str2[j - 1])
                cost = 0;
            else
                cost = 1;

            d[i, j] =
                Math.Min(
                    d[i - 1, j] + 1,              // Deletion
                    Math.Min(
                        d[i, j - 1] + 1,          // Insertion
                        d[i - 1, j - 1] + cost)); // Substitution

            if ((i > 1) && (j > 1) && (str1[i - 1] == 
                str2[j - 2]) && (str1[i - 2] == str2[j - 1]))
            {
                d[i, j] = Math.Min(d[i, j], d[i - 2, j - 2] + cost);
            }
        }
    }

    return d[str1.Length, str2.Length];
}

The Searching

In the search process, for each word in the wordlist, the Levenshtein-distance is computed, and with this distance, a score. This score represents how good the strings match. The input argument fuzzyness determines how much the strings can differ.

public static List<string> Search(
    string word,
    List<string> wordList,
    double fuzzyness)
{
    List<string> foundWords = new List<string>();

    foreach (string s in wordList)
    {
        // Calculate the Levenshtein-distance:
        int levenshteinDistance =
            LevenshteinDistance(word, s);

        // Length of the longer string:
        int length = Math.Max(word.Length, s.Length);

        // Calculate the score:
        double score = 1.0 - (double)levenshteinDistance / length;

        // Match?
        if (score > fuzzyness)
            foundWords.Add(s);
    }
    return foundWords;
}

LINQ-variant

This piece of code could be written in LINQ too.

public static List<string> Search(
    string word,
    List<string> wordList,
    double fuzzyness)
{
    // Tests have prove that the !LINQ-variant is about 3 times
    // faster!
    List<string> foundWords =
        (
            from s in wordList
            let levenshteinDistance = LevenshteinDistance(word, s)
            let length = Math.Max(s.Length, word.Length)
            let score = 1.0 - (double)levenshteinDistance / length
            where score > fuzzyness
            select s
        ).ToList();

    return foundWords;
}

History

• 2009 June 1^st: Initial release.