Abstract:The clustering characteristics of high-dimensional data are usually difficult to observe directly. Constructing it into a complex network, the topological structure of the network nodes can reflect the relationship between samples. Community detection of nodes in the network can achieve more intuitive clustering of data. A low randomness label propagation clustering algorithm based on network community detection is proposed. First, the data set is constructed as a sparse fully connected network using the radius and nearest neighbor methods. Then, according to the similarity of the nodes, the node labels are preprocessed to make the similar nodes have the same labels. The influence value of the nodes is used to improve the label propagation process and reduce the randomness of label selection. Finally, based on the cohesion, the community is optimized and merged to improve the quality of the community. The experimental results on real data sets and artificial data sets show that the algorithm has better adaptability to all kinds of data.