C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining criteria for tree-based regression and classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing multi-dimensional clusters, trends, and outliers using star coordinates
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Classification with Degree of Membership: A Fuzzy Approach
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Case Study: Visualization for Decision Tree Analysis in Data Mining
INFOVIS '01 Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS'01)
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constructive neural-network learning algorithms for pattern classification
IEEE Transactions on Neural Networks
Detecting Flaws and Intruders with Visual Data Analysis
IEEE Computer Graphics and Applications
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
VDM-RS: A visual data mining system for exploring and classifying remotely sensed images
Computers & Geosciences
Parallel Filter: A Visual Classifier Based on Parallel Coordinates and Multivariate Data Analysis
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
ACM SIGKDD Explorations Newsletter
Hifocon: object and dimensional coherence and correlation in multidimensional visualization
ISVC'05 Proceedings of the First international conference on Advances in Visual Computing
Web-based interface for the visualization of microarray data
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Interactive methods for exploring particle simulation data
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
Visualization of time-series data in parameter space for understanding facial dynamics
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
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Decision trees are commonly used for classification. We propose to use decision trees not just for classification but also for the wider purpose of knowledge discovery, because visualizing the decision tree can reveal much valuable information in the data. We introduce PaintingClass, a system for interactive construction, visualization and exploration of decision trees. PaintingClass provides an intuitive layout and convenient navigation of the decision tree. PaintingClass also provides the user the means to interactively construct the decision tree. Each node in the decision tree is displayed as a visual projection of the data. Through actual examples and comparison with other classification methods, we show that the user can effectively use PaintingClass to construct a decision tree and explore the decision tree to gain additional knowledge.