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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

Abstract

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.

Keywords

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

Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

References

  1. N. Soni, and A. Ganatra, "Categorization of Several Clustering Algorithms from Different Persepctive: A Review," International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 8, pp. 63-68, Aug., 2012.
  2. M. Ankerst, M. M. Breunig, H. Kriegel, and J. Sander, "OPTICS: Ordering Points To Identify the Clustering Structure," Proc. of the 26th ACM Special Interest Group on Management of Data, pp. 49-60, 1999.
  3. J. Sander, X. Qin, Z. Lu, N. Niu, and A. Kovarsky, "Automatic Extraction of Clusters from Hierarchical Clustering Representations," Proc. of the 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 75-87, 2003.
  4. R. J. G. B. Campello, D. Moulavi, and J. Sander, "Density-Based Clustering Based on Hierarchical Density Estimates," Proc. of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 160-172, 2013.
  5. M. F. Balcan, and P. Gupta, "Robust Hierarchical Clustering," Proc. of the 23rd Annual Conference on Learning Theory, pp. 282-294, 2010.
  6. B. Shneiderman, "Dynamic Queries for Visual Information Seeking," IEEE Software, Vol. 11, No. 6, pp. 70-77, Nov. 1994.
  7. J. Chen, A. M. MacEachren, and D. J. Peuquet, "Constructing Overview + Detail Dendrogram-Matrix Views," IEEE Trans. on Visualization and Computer Graphics, Vol. 15, No. 6, pp. 889-896, Nov. 2009. https://doi.org/10.1109/TVCG.2009.130
  8. A. Buja, J. A. McDonald, J. Michalak, and W. Stuetzle, "Interactive Data Visualization using Focusing and Linking," Proc. of the 2nd IEEE Conference on Visualization, pp. 156-163, 1991.
  9. H. Chang, and D.-Y. Yeung, "Robust Path-based Spectral Clustering," Pattern Recognition, Vol. 41, No. 1, pp. 191-203, Jan. 2008. https://doi.org/10.1016/j.patcog.2007.04.010
  10. J. Seo, and B. Shneiderman, "Interactively Exploring Hierarchical Clustering Results," IEEE Computer, Vol. 35, No. 7, pp. 80-86, Jul. 2002.
  11. E. Achtert, C. Bohm, and P. Kroger, "DeLi-Clu: Boosting Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking," Proc. of the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 119-128, 2006.
  12. E. Achtert, H. Kriegel, E. Schubert, and A. Zimek, "A Interactive Data Mining with 3D-Parallel- Coordinate-Trees," Proc. of the 40th ACM Special Interest Group on Management of Data, pp. 1009-1012, 2013.