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Table 1 Taxonomy of visualization techniques for visualizing clustering results.

From: XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

Principle to Show Cluster Membership Visualization Component Clustering Algorithm
Main Secondary   Hierarchical Partitional Density-based
Similarity
(color or size)
Proximity Scatterplot + Color Δ Δ [17] Δ [16]
   *Graph (vertex as item) + Color Δ Δ [5] Δ [16]
   *Bar chart (Reachability Plot) X X O [15, 20, 24]
  Enclosure Colored shape Δ [25] Δ Δ [13]
  . *Parallel coordinate plot + Color Δ [6] Δ Δ
Proximity Similarity and Enclosure Bar chart (Silhouettes Plot) Δ O [5, 14] O
Connectedness
(line connection)
Similarity and Proximity *Dendrogram O [2, 8, 9] X Δ
   Normal tree Δ [26] X Δ
   Circular tree Δ [26] X Δ
Enclosure Proximity *Heatmap + Partitioning O O O
  Similarity and Proximity Treemap Δ [26] X X
*Visualization techniques supported in XCluSim
  1. Visualization components for visualizing clustering results use visual cues based on Gestalt principles of grouping [27] to represent cluster membership. We categorize the visualization components by principle and indicate how appropriate each visualization component is for showing clustering results by different types of clustering algorithms. (i.e. "O" for most appropriate, "Δ" for moderately appropriate, and "X" for not applicable).