Summary. Our investigation
in this paper aims at
interactively exploring the decision tree results obtained by the
machine-learning algorithm like C4.5. We propose a new graphical radial
tree
layout method for supporting interactive exploration of decision trees.
A new
interactive graphical toolkit has been developed using explorer-like,
radial
layout, treemap, icicletree, focus+context, fisheye, zoom/pan,
hierarchical
visualization and interactive techniques to represent large decision
trees in a
graphical mode more intuitive than the results in output of the C4.5
algorithm.
The user can easily extract inductive rules and prune the tree in the
post-processing stage. He has a better understanding of the obtained
decision
tree models. The numerical test results with real datasets show that
the
proposed methods have given an insight into decision tree results.
Keywords: Post-processing Decision
trees, Interactive exploration, Visual data mining.
Decision tree
with 150 nodes for classifying Spambase
