Convexity algorithms in parallel coordinates
Journal of the ACM (JACM)
Curves and surfaces for computer aided geometric design: a practical guide
Curves and surfaces for computer aided geometric design: a practical guide
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
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 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
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth 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 association rules with interactive mosaic plots
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
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
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
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
A note on "beyond market baskets: generalizing association rules to correlations"
ACM SIGKDD Explorations Newsletter
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
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Visual Exploration of Frequent Itemsets and Association Rules
Visual Data Mining
FpViz: a visualizer for frequent pattern mining
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
FIsViz: a frequent itemset visualizer
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
FpVAT: a visual analytic tool for supporting frequent pattern mining
ACM SIGKDD Explorations Newsletter
CloseViz: visualizing useful patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
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
Parallel approaches to machine learning-A comprehensive survey
Journal of Parallel and Distributed Computing
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One fundamental problem for visualizing frequent itemsets and association rules is how to present a long border of frequent itemsets in an itemset lattice. Another problem comes from the lack of an effective visual metaphor to represent many-to-many relationships. This paper proposes an approach for visualizing frequent itemsets and many-to-many association rules by a novel use of parallel coordinates. An association rule is visualized by connecting items in the rule, one item on each parallel coordinate, with continuous polynomial curves. In the presence of item taxonomy, each coordinate can be used to visualize an item taxonomy tree which can be expanded or shrunk by user interaction. This user interaction introduces a border, which separates displayable itemsets from nondisplayable ones, in the generalized itemset lattice. Only those itemsets that are both frequent and displayable are considered to be displayed. This approach of visualizing frequent itemsets and association rules has the following features: 1) It is capable of visualizing many-to-many rules and itemsets with many items. 2) It is capable of visualizing a large number of itemsets or rules by displaying only those ones whose items are selected by the user. 3) The closure properties of frequent itemsets and association rules are inherently supported such that the implied ones are not displayed. Usefulness of this approach is demonstrated through examples.