Table 1.

Taxonomy of visualization techniques for visualizing clustering results.

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

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).