[1]封红旗,雷晨阳,沈田予,等.基于改进完全子图模型的关注对象多社区发现研究[J].南京理工大学学报(自然科学版),2016,40(06):674.[doi:10.14177/j.cnki.32-1397n.2016.40.06.006 ]
　Feng Hongqi,Lei Chenyang,Shen Tianyu,et al.Concerned objects multi-community detection based on improved complete subgraph model[J].Journal of Nanjing University of Science and Technology,2016,40(06):674.[doi:10.14177/j.cnki.32-1397n.2016.40.06.006 ]
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基于改进完全子图模型的关注对象多社区发现研究
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《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年06期
页码:
674
栏目:
出版日期:
2016-12-30

文章信息/Info

Title:
Concerned objects multi-community detection based on improved complete subgraph model
文章编号:
1005-9830(2016)06-0674-05
作者:
封红旗; 雷晨阳; 沈田予; 杨长春
常州大学 信息科学与工程学院,江苏 常州 213164
Author(s):
Feng Hongqi; Lei Chenyang; Shen Tianyu; Yang Changchun
School of Information Science and Engineering,Changzhou University,Changzhou 213164,China
关键词:
完全子图模型;  关注对象;  多类;  阈值划分;  数据挖掘算法
Keywords:
complete subgraph model;  concerned objects;  multi-classification;  threshold division;  data mining algorithm
分类号:
TP31
DOI:
10.14177/j.cnki.32-1397n.2016.40.06.006
摘要:
为实现用户和关注对象的多社区划分,针对完全子图模型不能进行多类分类的缺陷,该文引入了阈值划分方法,提出基于改进完全子图模型的社区发现算法。实验表明:与经典数据挖掘算法K-medoids相比,该文算法具有更高的准确性。
Abstract:
A multi-community detection method based on improved complete subgraph model is proposed using threshold division for multi-community division of users and concerned objects,because complete subgraph model cannot divide users and concerned objects based on multi-classification.Experiment result shows that compared with classical data mining algorithm K-medoids,this method is more accurate.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-07-03 修回日期:2016-10-23
基金项目:国家自然科学基金(61272367); 江苏省科技厅项目(BZ2010021); 江苏省研究生培养创新工程项目(20120515); 江苏省产学研前瞻性联合研究项目(BY2014037-08)
作者简介:封红旗(1967-),男,副研究员,主要研究方向:数据挖掘,E-mail:hqfeng@cczu.edu.cn。
引文格式:封红旗,雷晨阳,沈田予,等.基于改进完全子图模型的关注对象多社区发现研究[J].南京理工大学学报,2016,40(6):674-678.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2016-12-30