Comparison of Levenshtein Distance Algorithm and Needleman-Wunsch Distance Algorithm for String Matching

Abstract

String similarity measures play an increasingly important role in text related research and applications in tasks and operate on string sequences and character composition. A string metric is a metric that String_Based measures similarity or dissimilarity (distance) between two strings for approximate string matching or comparison. Determining similarity between texts is crucial to many applications such as clustering, duplicate removal, merging similar topics or themes, text retrieval and etc. String matching algorithms are used to find the similar characters between the source string and the target string. The proposed system is intended to match song information by comparing Levenshtein Distance Algorithm and Needleman-Wunch Distance Algorithm based on their f-score and execution time. So, user can search effectively their required song information by the title of songs or artist name using English language. Then the proposed system retrieves the user’s required song information with similarity score. The matching efficiencies of these algorithms are compared by searching f-score and the execution time. The proposed system uses song title and artist feature of billboard song dataset from year 1965-2015 and implements using Java programming language.