Toward Interactive Attribute Selection with Infolattices - A Position Paper

We discuss a new approach to interactive exploration of high-dimensional data sets which is aimed at building human’s understanding of the data by iterative additions of recommended attributes and objects that can together represent a context in which it may be useful to analyze the data. We identify challenges and expected benefits that our methodology can bring to the users. We also show how our ideas got inspired by Formal Concept Analysis (FCA) and Rough Set Theory (RST). It is though worth emphasizing that this particular paper is not aimed at investigating relationships between FCA and RST. Instead, the goal is to discuss which algorithmic methods developed within FCA and RST could be reused for the purpose of our approach.