A hierarchical clustering method based on a dynamic synchronization model
Huang Jian · Scientia Sinica Informationis · 2013
Clustering is an essential method for analyzing and mining the intrinsic group in data.This paper presents a novel synchronization-based hierarchical clustering method based on an extended Kuramoto dynamic synchronization model.Each data object is regarded as a phase oscillator and interacts dynamically with its neighboring objects.As time evolves,objects synchronize naturally.With regard to the local diameter of the neighborhood,the proposed method finds local synchronization-based natural clusters.Hierarchical clustering results are achieved by enlarging the local neighborhood distance of objects synchronizing continuously.Using a neighborhood closure,our method predicts clusters before the objects reach local synchronization,thereby significantly reducing the dynamic interaction time.To select the optimal clusters automatically,this hierarchical clustering method based on a dynamic synchronization model is combined with a clustering validation measure known as the silhouette width criterion.Combined with the silhouette width criterion,the proposed method is parameter-free.Moreover,the proposed method can detect clusters in data of arbitrary shapes,sizes and numbers without any data distribution assumptions.This synchronization-based clustering also allows natural outlier identification,since outliers do not synchronize with data objects in clusters.Extensive experiments on several synthetic and real-world data sets demonstrate that the proposed method achieves high clustering accuracy with lower execution time and fewer synchronization steps compared to the state-of-the-art method.