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.

翻译：新类别发现（Novel Class Discovery, NCD）是指在已标注的、与目标类别不同但相关的数据集中，从未标注数据中尝试发现新类别的任务。目前提出的大多数NCD方法仅处理图像数据，而表格数据却是实际应用中最广泛使用的数据类型之一。为解释聚类或NCD算法的结果，数据科学家需要理解表格数据中领域和任务特定的属性。这一任务极具挑战性，通常只能由领域专家完成。因此，本界面使领域专家能够轻松运行针对表格数据的最先进NCD算法，在仅需极少数据科学知识的前提下，即可生成可解释的结果。