Heuristic Approach to Improve the Efficiency of Maximum Weight Matching Algorithm Using Clustering

Abstract

Graph matching is one of the most well-studied problems in combinatorial optimization. The history of the maximum weight matching problem, which is one of the most popular problems in graph matching, is intertwined with the development of modern graph theory. To solve this problem, Edmonds proposed the blossom algorithm. After that, an increasing number of approximation matching algorithms that can be faster in matching than the blossom algorithm has been proposed. However, matched weights should be considered. This study aims to improve the efficiency of solving the maximum weight matching algorithm by using clustering. Improving efficiency means achieving optimal matching while improving the running time. In this experiment, we attempt to use various real-world datasets to ensure the accuracy of experiments.