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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

Concerned objects multi-community detection based on improved complete subgraph model
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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。 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.
作者 封红旗 雷晨阳 沈田予 杨长春
机构地区 常州大学信息科学与工程学院
出处 《南京理工大学学报》 EI CAS CSCD 北大核心 2016年第6期674-678,共5页 Journal of Nanjing University of Science and Technology
基金 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)
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 complete subgraph model concerned objects multi-classification threshold division data mining algorithm
分类号 TP31 [自动化与计算机技术—计算机软件与理论]
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南京理工大学学报
2016年 第6期

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