John Wenskovitch, Naren Ramakrishnan, Leanna L. House, Scotland C. Leman, Chris North


Dimension reduction algorithms and clustering algorithms are both frequently used techniques in visual analytics. Both families of algorithms assist analysts in performing related tasks regarding the similarity of observations and finding groups in datasets. Though initially used independently, recent works have incorporated algorithms from each family into the same visualization systems. However, these algorithmic combinations are often ad hoc or disconnected, working independently and in parallel rather than integrating some degree of interdependence. A number of design decisions must be addressed when employing dimension reduction and clustering algorithms concurrently in a visualization system, including the selection of each algorithm, the order in which they are processed, and how to present and interact with the resulting projection. This paper contributes an overview of combining dimension reduction and clustering into a visualization system, discussing the challenges inherent in developing a visualization system that makes use of both families of algorithms.


John Wenskovitch

Scotland C. Leman

Naren Ramakrishnan

Chris North

Leanna L. House

Publication Details

Date of publication:
August 29, 2017
IEEE Transactions on Visualization and Computer Graphics
Page number(s):
Publication note:

John E. Wenskovitch, Ian Crandell, Naren Ramakrishnan, Leanna House, Scotland Leman, Chris North: Towards a Systematic Combination of Dimension Reduction and Clustering in Visual Analytics. IEEE Trans. Vis. Comput. Graph. 24(1): 131-141 (2018)