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
Multidimensional lines I: representation
SIAM Journal on Applied Mathematics
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
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 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
Visualizing high dimensional datasets and multivariate relations (tutorial AM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth 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
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Pruning Redundant Association Rules Using Maximum Entropy Principle
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
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
Pruning and Visualizing Generalized Association Rules in Parallel Coordinates
IEEE Transactions on Knowledge and Data Engineering
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Frequent itemsets and association rules are defined on the powerset of a set of items and reflect the many-to-many relationships among the items. They bring technical challenges to information visualization which in general lacks effective visual technique to describe many-to-many relationships. This paper describes an approach for visualizing frequent itemsets and association rules by a novel use of parallel coordinates. An association rule is visualized by connecting its items, one on each parallel coordinate, with polynomial curves. In the presence of item taxonomy, an item taxonomy tree is displayed as coordinate and can be expanded or shrunk by user interaction. This interaction introduces a border in the generalized itemset lattice, which separates displayable itemsets from non-displayable ones. Only those frequent itemsets on the border are displayed. This approach can be generalized to the visualization of general monotone Boolean functions on lattice structure. Its usefulness is demonstrated through examples.