Name
Title | ||
|---|---|---|
Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data |
Abstract | ||
|---|---|---|
The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent n-D points losslessly, i.e., allowing the restoration of n-D data from the graphs. The projections of graphs are used for classification. The method is illustrated by solving machine-learning classification and dimension-reduction tasks from the domains of image processing, computer-aided medical diagnostics, and finance. Experiments conducted on several datasets show that this visual interactive method can compete in accuracy with analytical machine learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/informatics4030023 | INFORMATICS-BASEL |
Keywords | DocType | Volume |
interactive visualization, classification, clustering, dimension reduction, multidimensional visual analytics, machine learning, knowledge discovery, linear relations | Journal | 4 |
Issue | ISSN | Citations |
3 | 2227-9709 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Kovalerchuk | 1 | 235 | 50.77 |
| Dmytro Dovhalets | 2 | 0 | 0.34 |