Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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
Spotfire: an information exploration environment
ACM SIGMOD Record
Data mining: a hands-on approach for business professionals
Data mining: a hands-on approach for business professionals
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
DVIZ: A System for Visualizing Data Mining
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Discretization of Continuous Attributes for Learning Classification Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
Implementation Issues and Paradigms of Visual KDD Systems
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
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We introduce an interactive visualization system, CViz, for rule induction. The process of learning classification rules is visualized, which consists of five components: preparing and visualizing the original data, cleaning the original data, discretizing numerical attributes, learning classification rules, and visualizing the discovered rules. The CViz system is presented and each component is discussed. Three approaches for discretizing numerical attributes, including equal-length, equal-depth, and entropy-based approaches, are provided. The algorithm ELEM2 for learning classification rules is introduced, and the approaches to visualizing discretized data and classification rules are proposed. CViz could be easily adapted to visualize the rule induction process of other rule-based learning systems. Our experimental results on the IRIS data, Monks data, and artificial data show that the CViz system is useful and helpful for visualizing and understanding the learning process of classification rules.