| Abstract: | In this paper, we describe our progress in creating the framework for an
interactive application that allows humans to actively participate in a t-SNE clustering
process. t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality
reduction technique that maps high dimensional data sets to lower dimensions that can
then be visualized for human interpretation. By prompting users to monitor outlying
points during the t-SNE clustering process, we hypothesize that users may be able to make
clustering faster and more accurate than purely algorithmic methods. Further research
would test these hypotheses directly. We would also attempt to decrease the lag time
between the various components of our application and develop an intuitive approach for
humans to aid in clustering unlabeled data. Research into human assisted clustering can
combine the strengths of both humans and computer programs to improve the results of
data analysis. |
