C4.5: programs for machine learning
C4.5: programs for machine learning
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
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
AViz: A Visualization System for Discovering Numeric Association Rules
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Knowledge discovery interestingness measures based on unexpectedness
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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Association rules are a class of important regularities in databases. They are found to be very useful in practical applications. However, the number of association rules discovered in a database can be huge, thus making manual inspection and analysis of the rules difficult. In this paper, we propose a new framework to allow the user to explore the discovered rules to identify those interesting ones. This framework has two components, an interestingness analysis component, and a visualization component. The interestingness analysis component analyzes and organizes the discovered rules according to various interestingness criteria with respect to the user's existing knowledge. The visualization component enables the user to visually explore those potentially interesting rules. The key strength of the visualization component is that from a single screen, the user is able to obtain a global and yet detailed picture of various interesting aspects of the discovered rules. Enhanced with color effects, the user can easily and quickly focus his/her attention on the more interesting/useful rules.