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 is an important method of data mining for real data sets, but the effect of clustering analysis is not good. At this time, if we introduce the correlation measure and construct the data set into a complex network, we can use the community discovery method to process it. Most of the existing literatures focus on the improvement of the algorithm, and there is less research on the comparison of the results of the two methods. In this paper, we select the Louvain algorithm in community partition and K-means clustering algorithm in clustering algorithm. Firstly, we compare the theories of the two algorithms, and then use the data of patients with heart disease and kidney disease to construct a complex network. We compare the community partition results of the Louvain algorithm and the clustering results of the K-means clustering algorithm, The result of community partition of Louvain algorithm is better than that of K-means clustering algorithm.