Visualizing Cluster Hierarchy Using Hierarchy Generation Framework

계층 발생 프레임워크를 이용한 군집 계층 시각화

  • 신동화 (서울대학교 컴퓨터공학부) ;
  • 이세희 (서울대학교 컴퓨터공학부) ;
  • 서진욱 (서울대학교 컴퓨터공학부)
  • Received : 2015.03.25
  • Accepted : 2015.04.16
  • Published : 2015.06.15


There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.


visual analytics;hierarchical clustering;density-based clustering;hierarchy generation framework;information visualization;user interface


Supported by : National Research Foundation of Korea(NRF)


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