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

String similarity measures play an increasingly important role in text relatedresearch and applications in tasks and operate on string sequences and charactercomposition. A string metric is a metric that String_Based measures similarity ordissimilarity (distance) between two strings for approximate string matching orcomparison. Determining similarity between texts is crucial to many applications suchas clustering, duplicate removal, merging similar topics or themes, text retrieval andetc.String matching algorithms are used to find the similar characters between thesource string and the target string. The proposed system is intended to match songinformation by comparing Levenshtein Distance Algorithm and Needleman-WunchDistance Algorithm based on their f-score and execution time. So, user can searcheffectively their required song information by the title of songs or artist name usingEnglish language. Then the proposed system retrieves the user’s required songinformation with similarity score. The matching efficiencies of these algorithms arecompared by searching f-score and the execution time. The proposed system usessong title and artist feature of billboard song dataset from year 1965-2015 andimplements using Java programming language