Introduction to the theory of neural computation
Introduction to the theory of neural computation
Designing the user interface (2nd ed.): strategies for effective human-computer interaction
Designing the user interface (2nd ed.): strategies for effective human-computer interaction
C4.5: programs for machine learning
C4.5: programs for machine learning
Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments
Machine Learning - Special issue on evaluating and changing representation
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Exploring N-dimensional databases
VIS '90 Proceedings of the 1st conference on Visualization '90
Visualizing high-dimensional predicitive model quality
Proceedings of the conference on Visualization '00
Query, analysis, and visualization of hierarchically structured data using Polaris
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
Visualizing Sequential Patterns for Text Mining
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Visualization of association rules over relational DBMSs
Proceedings of the 2003 ACM symposium on Applied computing
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Decision tables, like decision trees or neural nets, are classification models used for prediction. They are induced by machine learning algorithms. A decision table consists of a hierarchical table in which each entry in a higher level table gets broken down by the values of a pair of additional attributes to form another table. The structure is similar to dimensional stacking. Presented here is a visualization method that allows a model based on many attributes to be understood even by those unfamiliar with machine learning. Various forms of interaction are used to make this visualization more useful than other static designs.