Issue No. 12 - Dec. (2016 vol. 28)
Laurence Anthony F. Park , School of Computing, Engineering and Mathematics, Western Sydney University, Rydalmere, NSW, Austraila
James C. Bezdek , Department of Computing and Information Systems, Parkville, University of Melbourne, VIC, Australia
Christopher Leckie , Department of Computing and Information Systems, Parkville, University of Melbourne, VIC, Australia
Ramamohanarao Kotagiri , Department of Computing and Information Systems, Parkville, University of Melbourne, VIC, Australia
James Bailey , Department of Computing and Information Systems, Parkville, University of Melbourne, VIC, Australia
Marimuthu Palaniswami , Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
The iVAT (asiVAT) algorithms reorder symmetric (asymmetric) dissimilarity data so that an image of the data may reveal cluster substructure. Images formed from incomplete data don't offer a very rich interpretation of cluster structure. In this paper, we examine four methods for completing the input data with imputed values before imaging. We choose a best method using contaminated versions of the complete Iris data, for which the desired results are known. Then, we analyze two real world data sets from social networks that are incomplete using the best imputation method chosen in the juried trials with Iris: (i) Sampson's monastery data, an incomplete, asymmetric relation matrix; and (ii) the karate club data, comprising a symmetric similarity matrix that is about 86 percent incomplete.
Iris, Visualization, Clustering algorithms, Symmetric matrices, Data visualization, Social network services, Iris recognition
L. A. Park, J. C. Bezdek, C. Leckie, R. Kotagiri, J. Bailey and M. Palaniswami, "Visual Assessment of Clustering Tendency for Incomplete Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 28, no. 12, pp. 3409-3422, 2016.