A Comparative Study of Louvain Algorithm and K-Means Clustering Algorithm

Complex network is a new research field in recent years, and community discovery is one of its application directions. Clustering analysis of real data sets is an important method of data mining, but there is a situation that the effect of clustering analysis is not good. At this time, if the correlation measure is introduced to construct the data set into a complex network, the community discovery method can be used to process it. Most of the existing literatures have improved the algorithm, and few studies have compared the results of the two methods. In this paper, the Louvain algorithm in community division and the K-means clustering algorithmin clustering algorithm are selected. First, the theories of the two algorithms are compared. Then, the complex network is constructed by using the data of patients with heart disease and kidney disease. The results of the community division of the Louvain algorithm and the clustering results of the K-means clustering algorithm are compared. Under the evaluation criteria of the correct division rate, the results of the community division of the Louvain algorithm are better than the clustering results of the K-means clustering algorithm.