Clustering is the process of grouping different data objects based on similar 
properties. Clustering has applications in various case studies from several 
fields such as graph theory, image analysis, pattern recognition, statistics and 
others. Nowadays, there are numerous algorithms and tools able to generate 
clustering results. However, different algorithms or parameterizations may 
produce quite dissimilar cluster sets. In this way, the user is often forced to 
manually filter and compare these results in order to decide which of them 
generate the ideal clusters. To automate this process, in this study, we present 
VICTOR, the first fully interactive and dependency-free visual analytics web 
application which allows the visual comparison of the results of various 
clustering algorithms. VICTOR can handle multiple cluster set results 
simultaneously and compare them using ten different metrics. Clustering results 
can be filtered and compared to each other with the use of data tables or 
interactive heatmaps, bar plots, correlation networks, sankey and circos plots. 
We demonstrate VICTOR's functionality using three examples. In the first case, 
we compare five different network clustering algorithms on a Yeast 
protein-protein interaction dataset whereas in the second example, we test four 
different parameters of the MCL clustering algorithm on the same dataset. 
Finally, as a third example, we compare four different meta-analyses with 
hierarchically clustered differentially expressed genes found to be involved in 
myocardial infarction. VICTOR is available at http://victor.pavlopouloslab.info 
or http://bib.fleming.gr:3838/VICTOR.