반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

입자 군집 최적화 알고리즘을 통한 K-평균 군집화 개선

Improvement of K-means Clustering Through Particle Swarm Optimization

  • 양경채 (금오공과대학교 컨설팅대학원) ;
  • 김민제 (금오공과대학교 산업공학부) ;
  • 이종환 (금오공과대학교 산업공학부)
  • Kyeong Chae Yang (Graduate School of Consulting, Kumoh National Institute of Technology) ;
  • Minje Kim (Department of Consulting Graduate School, Kumoh National Institute of Technology) ;
  • Jonghwan Lee (Department of Consulting Graduate School, Kumoh National Institute of Technology)
  • 투고 : 2024.07.15
  • 심사 : 2024.09.12
  • 발행 : 2024.09.30
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초록

Unsupervised learning is a type of machine learning, and unlike supervised learning or reinforcement learning, a target value for input value is not given. Clustering is mainly used for such unsupervised learning. One of the representative methods of such clustering is K-means clustering. Since K-means clustering is a method of determining the number of clusters and continuing to find the central point of the data allocated to the cluster, there is a problem that the clustered group may not be the optimal cluster. In this study, particle swarm optimization algorithm, which determines the motion vector by adding various variables as well as the center point, is applied to K-means clustering. The improved K-means clustering makes it possible to move toward better outcome values even when the center of cluster no longer change. In the conventional clustering method, the center of the cluster moves to the center of the data belonging to the cluster, and clustering ends when the cluster does not change, so other characteristics other than the center value are excluded. Unlike the conventional clustering method, the improved clustering method uses a central value, an average value, and a random value as variables, and a particle swarm optimization algorithm that modifies the vector for each iteration is applied. As a result, improved clustering method derived a better result value than the existing clustering method in the group's fitness index, silhouette score.

키워드

과제정보

This paper was supported by Kumoh National Institute of Technology (2022~2024).

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