C4.5: programs for machine learning
C4.5: programs for machine learning
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
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
Querying multiple sets of discovered rules
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
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
Opportunity map: a visualization framework for fast identification of actionable knowledge
Proceedings of the 14th ACM international conference on Information and knowledge management
Rule interestingness analysis using OLAP operations
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Opportunity map: identifying causes of failure - a deployed data mining system
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Data mining algorithms usually generate a large number of rules, which may not always be useful to human users. In this project, we propose a novel visual data-mining framework, called Opportunity Map, to identify useful and actionable knowledge quickly and easily from the discovered rules. The framework is inspired by the House of Quality from Quality Function Deployment (QFD) in Quality Engineering. It associates discovered rules, related summarized data and data distributions with the application objective using an interactive matrix. Combined with drill down visualization, integrated visualization of data distribution bars and rules, visualization of trend behaviors, and comparative analysis, the Opportunity Map allows users to analyze rules and data at different levels of detail and quickly identify the actionable knowledge and opportunities. The proposed framework represents a systematic and flexible approach to rule analysis. Applications of the system to large-scale data sets from our industrial partner have yielded promising results.