Into the Void: Mapping the Unseen Gaps in High Dimensional Data

We present a comprehensive pipeline, integrated with a visual analytics system called GapMiner, capable of exploring and exploiting untapped opportunities within the empty regions of high-dimensional datasets. Our approach utilizes a novel Empty-Space Search Algorithm (ESA) to identify the center points of these uncharted voids, which represent reservoirs for potentially valuable new configurations. Initially, this process is guided by user interactions through GapMiner, which visualizes Empty-Space Configurations (ESCs) within the context of the dataset and allows domain experts to explore and refine ESCs for subsequent validation in domain experiments or simulations. These activities iteratively enhance the dataset and contribute to training a connected deep neural network (DNN). As training progresses, the DNN gradually assumes the role of identifying and validating high-potential ESCs, reducing the need for direct user involvement. Once the DNN achieves sufficient accuracy, it autonomously guides the exploration of optimal configurations by predicting performance and refining configurations through a combination of gradient ascent and improved empty-space searches. Domain experts were actively involved throughout the system's development. Our findings demonstrate that this methodology consistently generates superior novel configurations compared to conventional randomization-based approaches. We illustrate its effectiveness in multiple case studies with diverse objectives.