Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Multidimensional Scaling by Deterministic Annealing
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Cluster Analysis
Visualization of similarities and dissimilarities in rules using multidimensional scaling
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
| Hi-index | 0.00 |
Association rules are typically evaluated in terms of support and confidence measures, which ensure that discovered rules have enough positive evidence. However, in real-world applications, even considering only those rules with high confidence and support it is not true that all of them are interesting. It may happen that the presentation of all discovered rules can discourage users from interpreting them in order to find nuggets of knowledge. Association rules interpretation can benefit from discovering group of “similar” rules, where (dis)similarity is estimated on the basis of syntactic or semantic characteristics. In this paper, we resort to the multi-dimensional scaling to support a visual exploration of association rules by means of bi-dimensional scatter-plots. An application in the domain of biomedical literature is reported. Results show that the use of this visualization technique is beneficial.