Mining decision trees from data streams in a mobile environment

This paper presents a novel Fourier analysis-based technique to aggregate, communicate and visualize decision trees in a mobile environment. A Fourier representation of a decision tree has several useful properties that are particularly useful for mining continuous data streams from small mobile computing devices. This paper presents algorithms to compute the Fourier spectrum of a decision tree and vice versa. It offers a framework to aggregate decision trees in their Fourier representations. It also describes a touchpad/ticker-based approach to visualize decision trees using their Fourier spectrum and an implementation for PDAs.