CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The perspective wall: detail and context smoothly integrated
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A focus+context technique based on hyperbolic geometry for visualizing large hierarchies
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
An experimental evaluation of transparent menu usage
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Visual exploration of large data sets
Communications of the ACM
SmartSkin: an infrastructure for freehand manipulation on interactive surfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
VL '96 Proceedings of the 1996 IEEE Symposium on Visual Languages
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Interactive Information Visualization of a Million Items
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
Emulating a Cooperative Behavior in a Generic Association Rule Visualization Tool
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
| Hi-index | 0.00 |
Knowledge Discovery in Databases (KDD) is an active research domain. Due to the number of large databases, various data mining methods were developed. Those tools can generate a large amount of knowledge that needs more advanced tools to be explored. We focus on association rules mining such as "If Antecedent then Conclusion" and more particularly on rules visualization during the post processing stage in order to help expert's analysis. An association rule is mainly calculated depending on two user-specified metrics: support and confidence. All current representations present a common limitation which is effective on small data quantities. We introduced a new interactive approach which combines both a global representation (2D matrix) and a detailed representation (Fisheyes view) in order to display large sets of association rules.