An Interactive Interface for Novel Class Discovery in Tabular Data
This addresses the difficulty for domain experts in understanding clustering outcomes in tabular data, which is widely used but often lacks specialized tools compared to image data.
The paper tackles the problem of interpreting novel class discovery results in tabular data by developing an interactive interface that allows domain experts to run state-of-the-art algorithms and generate interpretable results with minimal data science knowledge.
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.