ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model
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...
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Editorial Department of Journal on Communications
2014-11-01
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Series: | Tongxin xuebao |
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Online Access: | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.012/ |
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author | Xue-wen DONG Chao YANG Li-jie SHENG Jian-feng MA |
author_facet | Xue-wen DONG Chao YANG Li-jie SHENG Jian-feng MA |
author_sort | Xue-wen DONG |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-cd6a094d2c5c4a328445d39b51cdc4f6 |
institution | Kabale University |
issn | 1000-436X |
language | zho |
publishDate | 2014-11-01 |
publisher | Editorial Department of Journal on Communications |
record_format | Article |
series | Tongxin xuebao |
spelling | doaj-art-cd6a094d2c5c4a328445d39b51cdc4f62025-01-14T06:45:07ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2014-11-0135869359688964ESYN:efficient synchronization clustering algorithm based on dynamic synchronization modelXue-wen DONGChao YANGLi-jie SHENGJian-feng MAClustering 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.http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.012/clusteringsynchronization modelvectorizationmodularity |
spellingShingle | Xue-wen DONG Chao YANG Li-jie SHENG Jian-feng MA ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model Tongxin xuebao clustering synchronization model vectorization modularity |
title | ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model |
title_full | ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model |
title_fullStr | ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model |
title_full_unstemmed | ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model |
title_short | ESYN:efficient synchronization clustering algorithm based on dynamic synchronization model |
title_sort | esyn efficient synchronization clustering algorithm based on dynamic synchronization model |
topic | clustering synchronization model vectorization modularity |
url | http://www.joconline.com.cn/zh/article/doi/10.3969/j.issn.1000-436x.2014.z2.012/ |
work_keys_str_mv | AT xuewendong esynefficientsynchronizationclusteringalgorithmbasedondynamicsynchronizationmodel AT chaoyang esynefficientsynchronizationclusteringalgorithmbasedondynamicsynchronizationmodel AT lijiesheng esynefficientsynchronizationclusteringalgorithmbasedondynamicsynchronizationmodel AT jianfengma esynefficientsynchronizationclusteringalgorithmbasedondynamicsynchronizationmodel |