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@erwanschild erwanschild released this 05 Nov 15:44
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Interactive Clustering : Comparative Studies

Several comparative studies of cognitivefactory-interactive-clustering functionalities on NLP datasets.

Quick description of Interactive Clustering

Interactive clustering is a method intended to assist in the design of a training data set.

This iterative process begins with an unlabeled dataset, and it uses a sequence of two substeps :

  1. the user defines constraints on data sampled by the machine ;
  2. the machine performs data partitioning using a constrained clustering algorithm.

Thus, at each step of the process :

  • the user corrects the clustering of the previous steps using constraints, and
  • the machine offers a corrected and more relevant data partitioning for the next step.

Description of studies

Several studies are provided here:

  1. efficience: Aims to confirm the technical efficience of the method by verifying its convergence to a ground truth and by finding the best implementation to increase convergence speed.

Associated research article

Schild, E., Durantin, G., Lamirel, J., & Miconi, F. (2022). Iterative and Semi-Supervised Design of Chatbots Using Interactive Clustering. International Journal of Data Warehousing and Mining (IJDWM), 18(2), 1-19. http://doi.org/10.4018/IJDWM.298007. <hal-03648041>.

How to cite

Schild, E. (2021). cognitivefactory/interactive-clustering-comparative-study. Zenodo. https://doi.org/10.5281/zenodo.5648255

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