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Evolution of Intellectual Structure of Data Mining Research Based on Keywords

In: AI and Analytics for Smart Cities and Service Systems

Author

Listed:
  • Yue Huang

    (Beijing Language and Culture University)

  • Runyu Tian

    (Beijing Language and Culture University)

  • Yonghe Yang

    (Beijing Language and Culture University)

Abstract

Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. The purpose of this study is to detect the evolutional intellectual structure of data mining from the aspect of keywords. This study takes the 5,380 papers, published between 2007 and 2016 retrieved from 11 leading data mining journals defined by Google Scholar Metrics as the dataset. After data pre-processing, keyword frequency analysis is caried out to detect the three different developing patterns of keywords, which indicates that the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Then this paper constructs co-word matrices of high-frequency keywords of different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show that there are four main data mining topics, and the attention has been paid more to graph data mining and complex network analysis in the past five years.

Suggested Citation

  • Yue Huang & Runyu Tian & Yonghe Yang, 2021. "Evolution of Intellectual Structure of Data Mining Research Based on Keywords," Lecture Notes in Operations Research, in: Robin Qiu & Kelly Lyons & Weiwei Chen (ed.), AI and Analytics for Smart Cities and Service Systems, pages 125-140, Springer.
    • Handle: RePEc:spr:lnopch:978-3-030-90275-9_11
    DOI: 10.1007/978-3-030-90275-9_11
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