Name
Abstract | ||
|---|---|---|
In this paper we propose a method that combines the advanced data analysis of the automatic statistical methods and the flexibility and manual parameter tuning of interactive visual clustering. We present the Semi-Supervised Visual Clustering (SSVC) interface; its main contribution is the learning of the optimal projection distance metric for the star and spherical coordinate visualization systems. Beyond the conventional manual setting, it couples the visual clustering with the automatic setting where the projection distance metric is learned from the available set of user feedbacks in the form of either item similarities or direct item annotations. Moreover, SSVC interface allows for the hybrid setting where some parameters are manually set by the user while the remaining parameters are determined by the optimal distance algorithm. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1145/1363686.1363892 | SAC |
Keywords | Field | DocType |
optimal projection distance,automatic setting,hybrid setting,semi-supervised visual clustering,projection distance metric,conventional manual setting,optimal distance algorithm,direct item annotation,available set,automatic statistical method,ssvc interface,data analysis,visual system,interactive visualization,coordinate system,distance metric | Fuzzy clustering,Computer vision,Correlation clustering,Visualization,Computer science,Metric (mathematics),Artificial intelligence,Cluster analysis,Spherical coordinate system | Conference |
Citations | PageRank | References |
1 | 0.36 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Chidlovskii | 1 | 411 | 52.58 |
| Loïc Lecerf | 2 | 10 | 2.35 |