图书情报工作

图书情报工作 >

2015 , Vol. 59 >Issue 10: 6 - 13

DOI: https://doi.org/10.13266/j.issn.0252-3116.2015.10.001

专题：网络动态数据挖掘及其在企业竞争情报中的应用

网络动态数据挖掘研究进展与展望

  • 黄晓斌 ,
  • 张兴旺
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  • 1. 中山大学资讯管理学院 广州 510006;
    2. 桂林理工大学图书馆 桂林 541004
黄晓斌(ORCID:0000-0001-9178-7408),教授,博士生导师,E-mail:isshxb@mail.sysu.edu.cn;张兴旺(ORCID:0000-0002-6004-8642),副研究馆员,博士研究生。

收稿日期: 2015-04-07

  修回日期: 2015-05-10

  网络出版日期: 2015-05-20

基金资助

本文系国家社会科学基金项目"网页内容分析与挖掘的企业竞争情报方法研究"(项目编号:10BTQ034)研究成果之一。

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Dynamic Network Data Mining Progress and Prospects

  • Huang Xiaobin ,
  • Zhang Xingwang
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  • 1. School of Information Management, Sun Yat-Sen University, Guangzhou 510006;
    2. Library of Guilin University of Technology, Guilin 541004

Received date: 2015-04-07

  Revised date: 2015-05-10

  Online published: 2015-05-20

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摘要

[目的/意义]传统数据挖掘技术很难有效地对海量、高维、动态的网络数据进行分析,这成为当前商业智能、决策分析和知识发现等领域中的主要技术瓶颈,网络动态数据挖掘能有效解决这一问题。[方法/过程]通过梳理网络动态数据挖掘相关理论与应用研究成果,对网络动态数据挖掘研究的形成过程、发展历程和研究趋势进行归纳和总结。[结果/结论]研究认为:网络动态数据挖掘过程中突变性问题、与情报学的融合问题、社交网络动态演化模型、研究合作网络时序变化及网络动态数据挖掘中的粒度计算问题等是情报学研究需要关注的主要研究方向。

关键词： 网络动态数据挖掘; 数据流挖掘; 竞争情报

本文引用格式

黄晓斌 , 张兴旺 . 网络动态数据挖掘研究进展与展望[J]. 图书情报工作, 2015 , 59(10) : 6 -13 . DOI: 10.13266/j.issn.0252-3116.2015.10.001

Abstract

[Purpose/significance] Traditional data mining technology is difficult to effectively analyze the mass, high dimensional, dynamic network data, which becomes the main technical bottleneck in the field of business intelligence, decision analysis and knowledge discovery. Dynamic network data mining can effectively solve the bottleneck.[Method/process]By combing the theory and application of research results, the formation, development process and trends of dynamic network data mining research are summarized.[Result/conclusion]The study finds that the future research trends include the process of mutation research NDDM issues, integration NDDM and information science, social networking dynamic evolution model to study changes in the timing and NDDM cooperative network of granular computing problems.

Key words： dynamic network data mining; data stream mining; competitive intelligence

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