C4.5: programs for machine learning
C4.5: programs for machine learning
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth 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
RuleViz: a model for visualizing knowledge discovery process
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Information Visualization and Visual Data Mining
IEEE Transactions on Visualization and Computer Graphics
What Makes Patterns Interesting in Knowledge Discovery Systems
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
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
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
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
V-Miner: using enhanced parallel coordinates to mine product design and test data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Visual Data Mining Framework for Convenient Identification of Useful Knowledge
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Visual Data Mining: An Introduction and Overview
Visual Data Mining
Generating design knowledge though data mining
Journal of Computing Sciences in Colleges
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Data mining techniques frequently find a large number of patterns or rules, which make it very difficult for a human analyst to interpret the results and to find the truly interesting and actionable rules. Due to the subjective nature of "interestingness", human involvement in the analysis process is crucial. In this paper, we propose a novel visual data mining framework for the purpose of identifying actionable knowledge quickly and easily from discovered rules and data. This framework is called the Opportunity Map. It is inspired by some interesting ideas from Quality Engineering, in particular Quality Function Deployment (QFD) and the House of Quality. It associates summarized data or discovered rules with the application objective using an interactive matrix, which enables the user to quickly identify where the opportunities are. The proposed system can be used to visually analyze discovered rules, and other statistical properties of the data. The user can also interactively group actionable attributes and values, and see how they affect the targets of interest. Combined with drill-down and comparative analysis, the user can analyze rules and data at different levels of detail. The proposed visualization framework thus represents a systematic and yet flexible method of rule analysis. Applications of the system to large-scale data sets from our industrial partner have yielded promising results.