Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
interactions
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining and Data Visualization
Proceedings of the IEEE Visualization '95 Workshop on Database Issues for Data Visualization
Visualizing Decision Table Classifiers
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Architectures and optimizations for integrating data mining algorithms with database systems
Architectures and optimizations for integrating data mining algorithms with database systems
Database research at UT Arlington
ACM SIGMOD Record
Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships
Visual Data Mining
Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Graphical representation of defeasible logic rules using digraphs
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Visualizing defeasible logic rules for the semantic web
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
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The focus of this paper is the association rule visualization system that we have designed and developed. The rules produced by the mining algorithm are assumed to be stored in tables. The alternatives for visualization include tabular form, interactive two-dimensional, and three-dimensional graphics. By providing sorting and filtering abilities, the rule visualization system proposed in this paper provides a flexible, efficient, and easier way to manage and understand large number of association rules. As a result, this visualization system becomes an essential part of our association rule mining subsystem. We compare our association rule software with Intelligent Miner from IBM in various aspects, such as data accessibility, user interface, input/output, and rule visualization.