Machine Learning for Exploring Spatial Affordance Patterns
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This dissertation uses supervised and unsupervised data mining techniques to analyse office floor plans in an attempt to gain a better understanding of their geometry-to-function relationship. This question was deemed relevant after a background review of the state-of-the-art in automated floor-plan generation tools showed that such tools have been prototyped since the 1960s, but their search space is ill-informed because there are few formalisms to describe spatial affordance. To show and evaluate the relationship of geometry and use, data from visual graph analysis were used to train three supervised learners and compare these to a baseline accuracy established with a ZeroR classifier. This showed that for the office dataset examined, visual mean depth and integration are most tightly linked to usage and that the supervised learning algorithm J48 can correctly predict class performance on unseen examples to up to 79.5 case studies with unsupervised learners, which showed that use could not be immediately reverse-engineered based solemnly on the VGA information to achieve a strong cluster-to-class evaluation.
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