Published November 16, 2023 | Version 1.0.0
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cognitivefactory/interactive-clustering

Creators

Description

Interactive Clustering

Python package used to apply NLP interactive clustering methods.

  • GitHub repository : https://github.com/cognitivefactory/interactive-clustering/tree/1.0.0
  • Main documentation : https://cognitivefactory.github.io/interactive-clustering/
  • Pypi distribution : https://pypi.org/project/cognitivefactory-interactive-clustering/1.0.0/

Quick description

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 computer ;

  2. the computer 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 computer offers a corrected and more relevant data partitioning for the next step.

The process use severals objects :

  • a constraints manager : its role is to manage the constraints annotated by the user and to feed back the information deduced (such as the transitivity between constraints or the situation of inconsistency) ;

  • a constraints sampler : its role is to select the most relevant data during the annotation of constraints by the user ;

  • a constrained clustering algorithm : its role is to partition the data while respecting the constraints provided by the user.

References

  • PhD report: Schild, E. (2024, in press). De l'Importance de Valoriser l'Expertise Humaine dans l'Annotation : Application à la Modélisation de Textes en Intentions à l'aide d'un Clustering Interactif. Université de Lorraine. ;
  • First presentation: Schild, E., Durantin, G., Lamirel, J.C., & Miconi, F. (2021). Conception itérative et semi-supervisée d'assistants conversationnels par regroupement interactif des questions. In EGC 2021 - 21èmes Journées Francophones Extraction et Gestion des Connaissances. Edition RNTI. <hal-03133007>.
  • Theoretical study: 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>.
  • Methodological discussion: Schild, E., Durantin, G., & Lamirel, J.C. (2021). Concevoir un assistant conversationnel de manière itérative et semi-supervisée avec le clustering interactif. In Atelier - Fouille de Textes - Text Mine 2021 - En conjonction avec EGC 2021. <hal-03133060>.

Other links

  • Several comparative studies of Interactive Clustering methodology on NLP datasets: Schild, E. (2021). cognitivefactory/interactive-clustering-comparative-study. Zenodo. https://doi.org/10.5281/zenodo.5648255
  • A web application designed for NLP data annotation using Interactive Clustering methodology: Schild, E. (2021). cognitivefactory/interactive-clustering-gui. Zenodo. https://doi.org/10.5281/zenodo.4775270

How to cite

Schild, E. (2021). cognitivefactory/interactive-clustering. Zenodo. https://doi.org/10.5281/zenodo.4775251.

Files

cognitivefactory/interactive-clustering-1.0.0.zip

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Additional details

Related works

Is supplement to
Software: https://github.com/cognitivefactory/interactive-clustering/tree/1.0.0 (URL)

References

  • Schild, E. (2024, in press). De l'Importance de Valoriser l'Expertise Humaine dans l'Annotation : Application à la Modélisation de Textes en Intentions à l'aide d'un Clustering Interactif. Université de Lorraine.
  • Schild, E., Durantin, G., Lamirel, J.C., & Miconi, F. (2021). Conception itérative et semi-supervisée d'assistants conversationnels par regroupement interactif des questions. In EGC 2021 - 21èmes Journées Francophones Extraction et Gestion des Connaissances. Edition RNTI. <hal-03133007>.
  • 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>.
  • Schild, E., Durantin, G., & Lamirel, J.C. (2021). Concevoir un assistant conversationnel de manière itérative et semi-supervisée avec le clustering interactif. In Atelier - Fouille de Textes - Text Mine 2021 - En conjonction avec EGC 2021. <hal-03133060>.
  • Wagstaff, K. et C. Cardie (2000). Clustering with Instance-level Constraints. Proceedings of the Seventeenth International Conference on Machine Learning, 1103–1110.
  • Lampert, T., T.-B.-H. Dao, B. Lafabregue, N. Serrette, G. Forestier, B. Cremilleux, C. Vrain, et P. Gancarski (2018). Constrained distance based clustering for time-series : a comparative and experimental study. Data Mining and Knowledge Discovery 32(6), 1663–1707.
  • Frey, B. J., & Dueck, D. (2007). Clustering by Passing Messages Between Data Points. In Science (Vol. 315, Issue 5814, pp. 972–976). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.1136800
  • Givoni, I., & Frey, B. J. (2009). Semi-Supervised Affinity Propagation with Instance-Level Constraints. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:161-168
  • Ester, Martin & Kröger, Peer & Sander, Joerg & Xu, Xiaowei. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD. 96. 226-231.
  • Ruiz, Carlos & Spiliopoulou, Myra & Menasalvas, Ernestina. (2007). C-DBSCAN: Density-Based Clustering with Constraints. 216-223. 10.1007/978-3-540-72530-5_25.
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  • Khan, Md. A., Tamim, I., Ahmed, E., & Awal, M. A. (2012). Multiple Parameter Based Clustering (MPC): Prospective Analysis for Effective Clustering in Wireless Sensor Network (WSN) Using K-Means Algorithm. In Wireless Sensor Network (Vol. 04, Issue 01, pp. 18–24). Scientific Research Publishing, Inc. https://doi.org/10.4236/wsn.2012.41003.
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