Convexity algorithms in parallel coordinates
Journal of the ACM (JACM)
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining, hypergraph transversals, and machine learning (extended abstract)
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Global partial orders from sequential data
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Including the user in the knowledge discovery loop: interactive itemset-driven rule extraction
Proceedings of the 2008 ACM symposium on Applied computing
Visual Mining of Association Rules
Visual Data Mining
RadialViz: an orientation-free frequent pattern visualizer
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
| Hi-index | 0.00 |
Frequent itemsets, association rules and sequential patterns are defined on elements of power sets of items and reflect the many-to-many relationships among items. Although many tools have been developed to visualize association rules, none of them can simultaneously manage a large number of rules with multiple antecedents and multiple consequences. This problem is shown as a straightforward application of parallel coordinates. We show that, by properly arranging items on coordinates and by filtering out subsets of large frequent itemsets, item groups can be naturally displayed and that inherent properties such as partial orders in itemsets and in association rules are implied by this visualization paradigm. The usefulness of this approach is demonstrated through examples.