An Interactive Interface for Novel Class Discovery in Tabular Data

TL;DR

This paper introduces an interactive interface enabling domain experts to perform novel class discovery in tabular data using state-of-the-art algorithms, with minimal data science knowledge required.

Contribution

It presents a user-friendly tool that simplifies applying NCD algorithms to tabular data and enhances interpretability for domain experts.

Findings

Enables easy application of NCD algorithms to tabular data.

Provides interpretable results for domain experts.

Facilitates discovery of novel classes in practical datasets.

Abstract

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.