面向微博的多实体稀疏关系数据联合聚类

于淼; 杨武; 王巍; 申国伟

doi:10.11959/j.issn.1000-436x.2016019

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面向微博的多实体稀疏关系数据联合聚类

学术论文 | 更新时间：2024-06-05

- 面向微博的多实体稀疏关系数据联合聚类
- Co-clustering of multi-entities sparse relational data in microblogging
- 通信学报 2016年37卷第1期页码：151-159
- 作者机构：
  
  哈尔滨工程大学信息安全研究中心，黑龙江哈尔滨 150001
- 作者简介：
  
  [ "于淼（1987-），男，黑龙江牡丹江人，哈尔滨工程大学博士生，主要研究方向为数据挖掘、社会计算。" ]
  [ "杨武（1974-），男，辽宁宽甸人，博士，哈尔滨工程大学教授、博士生导师，主要研究方向为信息安全、数据挖掘、互联网安全。" ]
  [ "王巍（1974-），男，黑龙江哈尔滨人，博士，哈尔滨工程大学副教授，主要研究方向为数据挖掘、网络安全。" ]
  [ "申国伟（1986-），男，湖南邵阳人，哈尔滨工程大学博士生，主要研究方向为数据挖掘、信息安全。" ]
- 基金信息：
  
  国家高技术研究发展计（“863”计划）基金资助项目(2012AA012802)
- DOI：10.11959/j.issn.1000-436x.2016019
  中图分类号： TP393
- 网络出版日期：2016-01，
  
  纸质出版日期：2016-01-25
- 稿件说明：
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于淼, 杨武, 王巍, 等. 面向微博的多实体稀疏关系数据联合聚类[J]. 通信学报, 2016,37(1):151-159.

Miao YU, Wu YANG, Wei WANG, et al. Co-clustering of multi-entities sparse relational data in microblogging[J]. Journal on communications, 2016, 37(1): 151-159.
于淼, 杨武, 王巍, 等. 面向微博的多实体稀疏关系数据联合聚类[J]. 通信学报, 2016,37(1):151-159. DOI： 10.11959/j.issn.1000-436x.2016019.

Miao YU, Wu YANG, Wei WANG, et al. Co-clustering of multi-entities sparse relational data in microblogging[J]. Journal on communications, 2016, 37(1): 151-159. DOI： 10.11959/j.issn.1000-436x.2016019.

摘要

针对大规模微博中多实体间的稀疏关系数据，提出一种面向多实体稀疏关系数据的高效联合聚类算法。在算法中，为了充分利用多关系数据，提出了一种顽健的约束信息嵌入方法构建关系矩阵，降低了矩阵的稀疏性，进一步提高了算法的准确率。在稀疏约束的块坐标下降框架下，关系矩阵通过非负矩阵三分解算法同时获得不同实体的聚类指示矩阵。非负矩阵分解过程中，通过高效的投射算法实现快速求解，确保了聚类结果的稀疏结构。在人工和真实数据集上的实验表明，算法在个指标上都具有明显提高，特别是在极端稀疏数据上的效果更加明显。3

Abstract

For large-scale sparse relation data of multi-entity in microblogging

an efficient co-clustering algorithm was proposed which processed sparse relation data of multi-entity. In order to take full advantage of multi-relational data when using this algorithm

a robust constraint information embedding algorithm was proposed to construct relation ma-trix

and the performance of relation mining was improved by reducing matrix sparsity. In the sparse constraint block coordinate descent framework

relation matrix concurrently obtained cluster indication matrix of different entities by non-negative matrix tri-factorization. In non-negative matrix factorization

to ensure sparse structure of clustering result

a quick solution was achieved through efficient projection algorithm. Experiments on synthetic and real data sets show that proposed algorithm goes beyond all the baselines on three indicators. The improvement is more significant especially when processing extremely sparse data.

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references

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