  Novel Class Discovery (NCD) is the problem of trying to discover novel
classes in an unlabeled set, given a labeled set of different but related
classes. The majority of NCD methods proposed so far only deal with image data,
despite tabular data being among the most widely used type of data in practical
applications. To interpret the results of clustering or NCD algorithms, data
scientists need to understand the domain- and application-specific attributes
of tabular data. This task is difficult and can often only be performed by a
domain expert. Therefore, this interface allows a domain expert to easily run
state-of-the-art algorithms for NCD in tabular data. With minimal knowledge in
data science, interpretable results can be generated.
