Name
Abstract | ||
|---|---|---|
When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/IV.2013.23 | IV |
Keywords | Field | DocType |
overlapping community,overlapping communities,models overlapping community membership,network topology,large network topology,semi-supervised approach,clustering algorithm,hard clustering approach,overlapping clustering algorithm,own mental model,mental model,expert user,visualization,sorting,data visualisation | Data mining,Data structure,Data visualization,Computer science,Visualization,Fuzzy logic,Outlier,Sorting,Network topology,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick M. Dudas | 1 | 22 | 2.42 |
| Martijn de Jongh | 2 | 2 | 1.16 |
| Peter Brusilovsky | 3 | 5705 | 616.46 |