Enhancing Data Accuracy and Efficiency: An Overview of Fuzzy Matching Techniques

Abstract

: Fuzzy matching, also known as approximate string matching, is a powerful technique designed to improve data accuracy and efficiency by identifying and linking strings that exhibit partial similarity. Unlike traditional exact matching, which requires precise character - by - character agreement, fuzzy matching accounts for typographical errors, misspellings, and variations, allowing for a more flexible comparison. This paper presents an overview of fuzzy matching techniques and their applications across diverse domains. We delve into the core concepts of various algorithms, including Levenshtein distance, Jaccard similarity, soundex, and metaphone, exploring how each method quantifies the similarity between strings. The paper highlights their strengths and use cases in data cleaning, deduplication, information retrieval, natural language processing, record linkage, and named entity recognition.