Abstract
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.
| Original language | English (US) |
|---|---|
| Pages | 667-672 |
| Number of pages | 6 |
| DOIs | |
| State | Published - Dec 1 2003 |
| Event | 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States Duration: Aug 24 2003 → Aug 27 2003 |
Other
| Other | 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 8/24/03 → 8/27/03 |
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Keywords
- Classification
- Decision trees
- Information visualization
- Interactive visualization
- Visual data mining
ASJC Scopus subject areas
- Software
- Information Systems
Cite this
PaintingClass : Interactive construction, visualization and exploration of decision trees. / Teoh, Soon Tee; Ma, Kwan-Liu.
2003. 667-672 Paper presented at 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, Washington, DC, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - PaintingClass
T2 - Interactive construction, visualization and exploration of decision trees
AU - Teoh, Soon Tee
AU - Ma, Kwan-Liu
PY - 2003/12/1
Y1 - 2003/12/1
N2 - 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.
AB - 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.
KW - Classification
KW - Decision trees
KW - Information visualization
KW - Interactive visualization
KW - Visual data mining
UR - http://www.scopus.com/inward/record.url?scp=70349820456&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70349820456&partnerID=8YFLogxK
U2 - 10.1145/956750.956837
DO - 10.1145/956750.956837
M3 - Paper
SP - 667
EP - 672
ER -