ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model

Xue-wen DONG; Chao YANG; Li-jie SHENG; Jian-feng MA

doi:10.3969/j.issn.1000-436x.2014.z2.012

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ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model

Papers | 更新时间：2024-06-05

- ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model
- Journal on Communications Vol. 35, Issue Z2, Pages: 86-93(2014)
- 作者机构：
  
  西安电子科技大学计算机网络与系统安全陕西省重点实验室，陕西西安 710071) 2.西安电子科技大学计算机学院，陕西西安 710071
- 作者简介：
- 基金信息：
  
  The Program for Changjiang Scholars and Innovative Research Team in University(IRT1078);The Major National Science and Technology Program(2011ZX03005-002);The National Natural Science Foundation of China(61303219);The Natural Science Basic Research Plan in Shaanxi Province(2014JQ8297);The Natural Science Basic Research Plan in Shaanxi Province(2014JQ8295);The Fundamental Research Funds for the Central Universities(Y10000903006);The Fundamental Research Funds for the Central Universities(K5051303007)
- DOI：10.3969/j.issn.1000-436x.2014.z2.012
  CLC： TP181
- Online First：2014-11，
  
  Published：30 November 2014
- 稿件说明：
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Xue-wen DONG, Chao YANG, Li-jie SHENG, et al. ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model[J]. Journal on Communications, 2014, 35(Z2): 86-93.
DOI：

Xue-wen DONG, Chao YANG, Li-jie SHENG, et al. ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model[J]. Journal on Communications, 2014, 35(Z2): 86-93. DOI： 10.3969/j.issn.1000-436x.2014.z2.012.

摘要

基于动态同步模型，提出一种高效同步聚类ESYN算法。首先，根据非矢量网络的局部结构信息，提出节点相似度的定义，以准确描述节点间的链接密度；其次，利用OPTICS算法进行矢量化预处理，将非矢量网络转换为一维坐标序列；最后，在通用 Kuramoto 动态同步模型中，增加基于全局信息的耦合强度分析，同时不断增加同步半径，自动选取最优的聚类结果。在大量人工合成数据集和真实数据集上的实验结果表明算法聚类准确率较高。

Abstract

Clustering is an important research field in data mining.Based on dynamical synchronization model

an efficient synchronization clustering algorithm ESYN is proposed.Firstly

based on local structure information of a non-vector network

a new concept vertex similarity is brought up to describe the link density between vertices.Secondly

the network is vectoried by OPTICS algorithm and turned into one-dimensional coordination sequence.Finally

global coupling analysis is applied to generalized Kuramoto synchronization model

synchronization radius is increased and the optimal clustering result is automatically selected.The experimental results on a large number of synthetic and real-world networks show that proposed algorithm achieves high accuracy.

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references

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