Adaptive Binary Trees Visualization with Respect to User-Specified Quality Measures

Abstract

Many algorithms have been designed to visualize binary trees efficiently with respect to a quality measure. While each algorithm is suitable for drawing particular categories of binary trees, an effort to compile these algorithms to maximize the quality of drawings has not been realized. Our first step is to create a system that determines the type of a binary tree and then selects an algorithm to draw the tree depending upon the specified quality measures. Currently, our system recognizes six types of binary trees (AVL, Complete, Fibonacci, Random, Unbalanced-tothe- left, Unbalanced-to-the-right) and allows the user to choose from eleven quality measures (Area, Aspect Ratio, Total Edge Length, Maximum Edge Length, Uniform Edge Length, Closest Leaf, Farthest Leaf, Size, Minimum Angle Size, Average Angle Size, Angular Resolution). Experiments show that our adaptive visualization system outperforms any system using a single binary tree drawing algorithm. In addition, our approach allows the user to select multiple quality measures and automatically detects the best available binary tree drawing algorithm.