A Clustering Algorithm Based on Minimum Spanning Tree and Density
Jiayao Chen, Jing Xiao Lu · 2019
A Minimum spanning tree (MST) clustering algorithm based on density is proposed under the background of no effective support on the dataset with noises. Split and merge stages are employed for the proposed clustering algorithm. Furthermore, density estimation method is designed for split stage, and maximal connected sub graph is employed for merge stage. The experiments are performed on synthetic and real datasets, and clustering results show the proposed clustering algorithm can detect clusters with noises. Moreover, clusters with complex shapes can be recognized as well and it demonstrates the proposed clustering algorithm is robust to noises.