Evolution of Intellectual Structure of Data Mining Research Based on Keywords

Yue Huang (), Runyu Tian and Yonghe Yang
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Yue Huang: Beijing Language and Culture University
Runyu Tian: Beijing Language and Culture University
Yonghe Yang: Beijing Language and Culture University

A chapter in AI and Analytics for Smart Cities and Service Systems, 2021, pp 125-140 from Springer

Abstract: 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.

Keywords: Data mining; Intellectual structure; Co-word analysis; Clustering; Evolution analysis (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-030-90275-9_11

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DOI: 10.1007/978-3-030-90275-9_11

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