Comparison and Analysis of Several Synchronization Clustering Models

EasyChair Preprint no. 1980

Abstract

Clustering is an important data analysis and preprocessing technology. Compared with the traditional static clustering analysis methods, the clustering algorithms based on synchronization models are a kind of dynamic evolutionary clustering analysis technique. In this paper, two exponential decay weighted synchronization models and a δ near neighbor exponential decay weighted synchronization model for clustering are proposed. For the first two synchronization models, a clustering algorithm based on exponential decay weighted synchronization model is proposed. For the latter synchronization model, the extended Kuramoto model, a Vicsek simplified model, and a linear version of the Vicsek model, a clustering algorithm based on near neighbor synchronization model is proposed. The algorithm complexity, properties, and characteristics of these synchronous clustering models are compared and analyzed. In the simulation experiments of some artificial data set and eight UCI data sets, these synchronization clustering models were compared in clustering accuracy and clustering speed. Finally, it summarizes the development of some synchronization clustering algorithms and presents the next work.

Keyphrases: 同步模型, 指数衰减, 聚类, 近邻

@Booklet{EasyChair:1980,
  author = {Xinquan Chen},
  title = {Comparison and Analysis of Several Synchronization Clustering Models},
  howpublished = {EasyChair Preprint no. 1980},

  year = {EasyChair, 2019}}