K-Nearest Neighbor Implementation for Strings (Unstructured data) in Java

Question

I'm looking for implementation for K-Nearest Neighbor algorithm in Java for unstructured data. I found many implementation for numeric data, however how I can implement it and calculate the Euclidean Distance for text (Strings).

Here is one example for double:

public static double EuclideanDistance(double [] X, double []Y)
{
    int count = 0;
    double distance = 0.0;
    double sum = 0.0;
    if(X.length != Y.length)
    {
        try {
            throw new Exception("the number of elements" + 
                      " in X must match the number of elements in Y");
        } catch (Exception e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
    }
    else
    {
        count = X.length;
    }
    for (int i = 0; i < count; i++)
    {
        sum = sum + Math.pow(Math.abs(X[i] - Y[i]),2);
    }
    distance = Math.sqrt(sum);
    return distance;
}

How I can implement it for Strings (unstructured data)? For example, Class 1: "It was amazing. I loved it" "It is perfect movie"

Class 2: "Boring. Boring. Boring." "I do not like it"

How can we implement KNN on such type of data and calculate Euclidean Distance?

Answer 1

You correctly noticed that the only thing you have to do is to define the notion of distance between your strings. The problem is that it is task dependent. It can be anything from let's assign the distance to 1 if both strings have a world 'data' in it and 0 otherwise to something more complex like Okapi BM25.

Take a look at various string metrics or may be python implementation of tf-idf.