通信学报 ›› 2014, Vol. 35 ›› Issue (Z2): 86-93.doi: 10.3969/j.issn.1000-436x.2014.z2.012

• 学术论文 • 上一篇    下一篇

ESYN:基于动态模型的高效同步聚类算法

董学文,杨超,盛立杰,马建峰   

  1. 1.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
  • 出版日期:2014-11-25 发布日期:2017-06-19
  • 基金资助:
    长江学者和创新团队发展计划基金资助项目;国家自然基金委员会—广东联合基金重点基金资助项目;国家科技部重大专项基金资助项目;国家自然科学基金青年基金资助项目;陕西省自然科学基金资助项目;陕西省自然科学基金资助项目;中央高校基本科研业务费专项基金资助项目;中央高校基本科研业务费专项基金资助项目

ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model

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

  1. 1.Shaanxi Key Laboratory of Network and System Security,Xidian University,Xi’an 710071,China;2.School of Computer Science and Technology,Xidian University,Xi’an 710071,China
  • Online:2014-11-25 Published:2017-06-19
  • Supported by:
    The Program for Changjiang Scholars and Innovative Research Team in University;The Major National S & T Program;The National Natural Science Foundation of China;The Natural Science Basic Research Plan in Shaanxi Province;The Natural Science Basic Research Plan in Shaanxi Province;The Fundamental Research Funds for the Central Universities;The Fundamental Research Funds for the Central Universities

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。

关键词: 聚类, 同步模型, 矢量化, 模块度

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.

Key words: clustering, synchronization model, vectorization, modularity

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