An analytic process is iterative between two agents, an analyst and an analytic toolbox. Each iteration comprises three main steps: preparing a dataset, running an analytic tool, and evaluating the result, where dataset preparation and result evaluation, conducted by the analyst, are largely domain-knowledge driven. In this work, the focus is on automating the result evaluation step. The underlying problem is to identify plots that are deemed interesting by an analyst. We propose a methodology to learn such analyst's intent based on Generative Adversarial Networks (GANs) and demonstrate its applications in the context of production yield optimization using data collected from several product lines.
@article{arxiv.1807.03920,
title = {Discovering Interesting Plots in Production Yield Data Analytics},
author = {Matthew Nero and Chuanhe Shan and Li-C. Wang and Nik Sumikawa},
journal= {arXiv preprint arXiv:1807.03920},
year = {2018}
}