Name
Abstract | ||
|---|---|---|
The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/j.jvlc.2003.06.002 | Journal of Visual Languages & Computing |
Keywords | Field | DocType |
Visual data mining,Databases,Association rules,Classification | Data science,Information flow (information theory),Data mining,Data stream mining,Concept mining,Information retrieval,Visualization,Computer science,Visual analytics,Association rule learning,Interactive visual analysis,Exploratory data analysis | Journal |
Volume | Issue | ISSN |
14 | 6 | 1045-926X |
Citations | PageRank | References |
14 | 0.99 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ioannis Kopanakis | 1 | 264 | 16.68 |
| Babis Theodoulidis | 2 | 353 | 76.60 |