Natural Neighbor Clustering Algorithm without Boundary

Abstract

Most density-based clustering algorithms are only suitable for spherical data set. When processing streamlined data sets without cluster centers, the clustering results have certain defects. In order to deal with the clustering problem of streamlined data sets, the concept of natural neighbors and outlier detection are combined, and a boundary-removing natural neighbor clustering (NNC_wbo) algorithm is proposed. First, establish the natural neighbor relationship between the KD tree search data, calculate the intra-group density and intra-group outlier degree of the data points, set the parameters to remove the boundary data; then use the natural neighbor relationship to obtain the preliminary clustering results; if after the preliminary clustering, There are small clusters composed of very few data points, and outliers are excluded.