An Empirical Analysis Approach to Investigating Effectiveness of the PSO-based Clustering Method for Scholarly Papers Supported by the Research Grant Projects

개선된 PSO방법에 의한 학술연구조성사업 논문의 효과적인 분류 방법과 그 효과성에 관한 실증분석

  • 이건창 (성균관대학교 경영학부) ;
  • 서영욱 (성균관대학교 경영연구소) ;
  • 이대성 (성균관대학교 경영학과)
  • Received : 2009.09.18
  • Accepted : 2009.10.08
  • Published : 2009.12.01


This study is concerned with suggesting a new clustering algorithm to evaluate the value of papers which were supported by research grants by Korea Research Fund (KRF). The algorithm is based on an extended version of a conventional PSO (Particle Swarm Optimization) mechanism. In other words, the proposed algorithm is based on integration of k-means algorithm and simulated annealing mechanism, named KASA-PSO. To evaluate the robustness of KASA-PSO, its clustering results are evaluated by research grants experts working at KRF. Empirical results revealed that the proposed KASA-PSO clustering method shows improved results than conventional clustering method.


Supported by : National Research Foundation of Korea