An Interactive Interface for Novel Class Discovery in Tabular Data

Colin Troisemaine, Joachim Flocon-Cholet, Stéphane Gosselin, Alexandre Reiffers-Masson, Sandrine Vaton +1 more
6/22/2023
cs.LGcs.HC

Official code: ColinTr/InteractiveClustering (2 stars, last commit 2024-08-20).

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.

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Code Implementations(1)

ColinTr/InteractiveClusteringOfficial

[ECML PKDD 2023] An Interactive Interface for Novel Class Discovery in Tabular Data

20CSS, HTMLFeb 26, 20232 years agoNOASSERTION
class-discoveryclusteringdeep-learningncdnovel-class-discovery

Cite this paper

@article{troisemaine2023interactive,
  title  = {An Interactive Interface for Novel Class Discovery in Tabular Data},
  author = {Colin Troisemaine and Joachim Flocon-Cholet and Stéphane Gosselin and Alexandre Reiffers-Masson and Sandrine Vaton and Vincent Lemaire},
  year   = {2023},
  eprint = {2306.12919},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.12919v1}
}

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