Faster Algorithm for String Comparison
Title: Faster Algorithm for String Comparison
Abstract: This research aimed to develop a faster algorithm for string comparison. The study built upon previous work that used a token-based approach to determine string similarity, which had proven to be effective but could be further improved. The new algorithms introduced in this paper achieved higher accuracy and faster time complexity than the previous approach. The results showed that the proposed algorithms could significantly improve the accuracy and time efficiency of string comparison, making them suitable for various applications such as pattern recognition, computational biology, and data cleaning.
Main Research Question: Can we develop a faster algorithm for string comparison that maintains or improves accuracy?
Methodology: The study used a comparative approach to evaluate the performance of the new algorithms against the previous token-based approach. The algorithms were designed to work on substrings and were able to achieve time complexity O(knm) for the worst case, O(β*n) for the average case, and O(1) for the best case. Concrete examples and experimental results were provided to demonstrate the effectiveness of the new algorithms.
Results: The results showed that the new algorithms performed better in terms of time complexity and accuracy compared to the previous token-based approach. The algorithms were able to achieve higher accuracy while maintaining a faster time complexity.
Implications: The development of these new algorithms has significant implications for various fields that rely on string comparison, such as pattern recognition, computational biology, and data cleaning. The improved time efficiency and accuracy of the algorithms make them suitable for large-scale applications where speed is crucial. Furthermore, the algorithms' ability to retain the original semantic information makes them ideal for applications that require preserving the meaning of the strings being compared.
Link to Article: https://arxiv.org/abs/0112022v1 Authors: arXiv ID: 0112022v1