SHC: A Spectral Algorithm for Hierarchical Clustering
Xiaohong Li, Jingwei Huang · 2009
Hierarchical clustering (HC) is a widely used approach both in pattern recognition and data mining and has rich solutions in the literature. But all these existing solutions have some restrictions when the clustered dataset has complex structure. Spectral clustering is a graph-based, simple and outperforming method with the ability to find complex structure in dataset using spectral properties of the dataset-associated affinity matrix. In this paper, we propose a novel effective HC algorithm called SHC base on the techniques of spectral method. The experiment results both on artificial and real data sets show that our algorithm can hierarchically cluster complex data effectively and naturally.