Ritesh Mehta, Diane J. Cook, and Lawrence B. Holder
The goal of the MavHome smart home project is to build an intelligent home environment that is aware of its inhabitants and their activities. Such a home is designed to provide maximum comfort to inhabitants at minimum cost. This can be done by learning the activities of the inhabitants and to automate those activities. For this it is necessary to identify among multiple inhabitants who is currently present in the home. Subdue is a graph-based data mining algorithm that discovers patterns in structural data. By representing the activity patterns for each inhabitant as graphs, Subdue can be used for inhabitant identification. We introduce a multiple-class learning version of Subdue and show some preliminary results on synthetic smart home activity data for multiple inhabitants.