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Trend Analysis of Data Mining Research Using Topic Network Analysis

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2016, 21(5), pp.141-148
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Hyon Hee Kim 1 Rhee Hey Young 1

1동덕여자대학교

Accredited

ABSTRACT

In this paper, we propose a topic network analysis approach which integrates topic modeling and social network analysis. We collected 2,039 scientific papers from five top journals in the field of data mining published from 1996 to 2015, and analyzed them with the proposed approach. To identify topic trends, time-series analysis of topic network is performed based on 4 intervals. Our experimental results show centralization of the topic network has the highest score from 1996 to 2000, and decreases for next 5 years and increases again. For last 5 years, centralization of the degree centrality increases, while centralization of the betweenness centrality and closeness centrality decreases again. Also, clustering is identified as the most interrelated topic among other topics. Topics with the highest degree centrality evolves clustering, web applications, clustering and dimensionality reduction according to time. Our approach extracts the interrelationships of topics, which cannot be detected with conventional topic modeling approaches, and provides topical trends of data mining research fields.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.