Interactive Clustering¶

Python package used to apply NLP interactive clustering methods.

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 :

the user defines constraints on data sampled by the computer ;
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.

Simplified diagram of how Interactive Clustering works.

Example of iterations of Interactive Clustering.

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.

NB :

For more details, read the Documentation and the articles in the References section.
This python library does not contain integration into a graphic interface. A web application based on Interactive Clustering Methodologoy is available on GitHub: cognitivefactory/interactive-clustering-gui. For more details, read its main documentation.

Documentation¶

Main documentation

Installation¶

Interactive Clustering requires Python 3.8 or above.

To install with pip:

# install package
python3 -m pip install cognitivefactory-interactive-clustering

# install spacy language model dependencies (the one you want, with version "3.4.x")
python3 -m spacy download fr_core_news_md-3.4.0 --direct

To install with pipx:

# install pipx
python3 -m pip install --user pipx

# install package
pipx install --python python3 cognitivefactory-interactive-clustering

# install spacy language model dependencies (the one you want, with version "3.4.x")
python3 -m spacy download fr_core_news_md-3.4.0 --direct

NB : Other spaCy language models can be downloaded here : spaCy - Models & Languages. Use spacy version "3.4.x".

Development¶

To work on this project or contribute to it, please read:

the Copier PDM template documentation ;
the Contributing page for environment setup and development help ;
the Code of Conduct page for contribution rules.

References¶

Interactive Clustering:
- 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>.
Constraints and Constrained Clustering:
- Constraints in clustering: Wagstaff, K. et C. Cardie (2000). Clustering with Instance-level Constraints. Proceedings of the Seventeenth International Conference on Machine Learning, 1103–1110.
- Survey on Constrained Clustering: 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.
- Affinity Propagation:
  - Affinity Propagation Clustering: 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
  - Constrained Affinity Propagation Clustering: 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
- DBScan:
  - DBScan Clustering: 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.
  - Constrained DBScan Clustering: Ruiz, Carlos & Spiliopoulou, Myra & Menasalvas, Ernestina. (2007). C-DBSCAN: Density-Based Clustering with Constraints. 216-223. 10.1007/978-3-540-72530-5_25.
- KMeans Clustering:
  - KMeans Clustering: MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifth Berkeley symposium on mathematical statistics and probability 1(14), 281–297.
  - Constrained 'COP' KMeans Clustering: Wagstaff, K., C. Cardie, S. Rogers, et S. Schroedl (2001). Constrained K-means Clustering with Background Knowledge. International Conference on Machine Learning
  - Constrained 'MPC' KMeans Clustering: 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
- Hierarchical Clustering:
  - Hierarchical Clustering: Murtagh, F. et P. Contreras (2012). Algorithms for hierarchical clustering : An overview. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 2, 86–97.
  - Constrained Hierarchical Clustering: Davidson, I. et S. S. Ravi (2005). Agglomerative Hierarchical Clustering with Constraints : Theoretical and Empirical Results. Springer, Berlin, Heidelberg 3721, 12.
- Spectral Clustering:
  - Spectral Clustering: Ng, A. Y., M. I. Jordan, et Y.Weiss (2002). On Spectral Clustering: Analysis and an algorithm. In T. G. Dietterich, S. Becker, et Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems 14. MIT Press.
  - Constrained 'SPEC' Spectral Clustering: Kamvar, S. D., D. Klein, et C. D. Manning (2003). Spectral Learning. Proceedings of the international joint conference on artificial intelligence, 561–566.
Preprocessing and Vectorization:
- spaCy: Honnibal, M. et I. Montani (2017). spaCy 2 : Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing.
  - spaCy language models: https://spacy.io/usage/models
- NLTK: Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O’Reilly Media Inc.
  - NLTK 'SnowballStemmer': https://www.nltk.org/api/nltk.stem.html#module-nltk.stem.snowball
- Scikit-learn: Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R.Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, et E. Duchesnay (2011). Scikit-learn : Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.
  - Scikit-learn 'TfidfVectorizer': https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html

How to cite¶

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