# Interactive Clustering
[](https://github.com/cognitivefactory/interactive-clustering/actions?query=workflow%3Aci)
[](https://cognitivefactory.github.io/interactive-clustering/)
[](https://pypi.org/project/cognitivefactory-interactive-clustering/)
[](https://doi.org/10.5281/zenodo.4775251)
Python package used to apply NLP interactive clustering methods.
## <a name="Description"></a> 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.
_NB_ :
- This python library does not contain integration into a graphic interface.
- For more details, read the [Documentation](#Documentation) and the articles in the [References](#References) section.
## <a name="Documentation"></a> Documentation
- [Main documentation](https://cognitivefactory.github.io/interactive-clustering/)
## <a name="Installation"></a> Installation
Interactive Clustering requires Python 3.8 or above.
To install with [`pip`](https://github.com/pypa/pip):
```bash
# 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`](https://github.com/pypa/pipx):
```bash
# 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](https://spacy.io/usage/models). Use spacy version `"3.4.x"`.
## <a name="Development"></a> Development
To work on this project or contribute to it, please read:
- the [Copier PDM](https://pawamoy.github.io/copier-pdm/) template documentation ;
- the [Contributing](https://cognitivefactory.github.io/interactive-clustering/contributing/) page for environment setup and development help ;
- the [Code of Conduct](https://cognitivefactory.github.io/interactive-clustering/code_of_conduct/) page for contribution rules.
## <a name="References"></a> 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`
## <a name="Other links"></a> 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`
## <a name="How to cite"></a> How to cite
`Schild, E. (2021). cognitivefactory/interactive-clustering. Zenodo. https://doi.org/10.5281/zenodo.4775251.`
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"description": "# Interactive Clustering\n\n[](https://github.com/cognitivefactory/interactive-clustering/actions?query=workflow%3Aci)\n[](https://cognitivefactory.github.io/interactive-clustering/)\n[](https://pypi.org/project/cognitivefactory-interactive-clustering/)\n[](https://doi.org/10.5281/zenodo.4775251)\n\nPython package used to apply NLP interactive clustering methods.\n\n\n## <a name=\"Description\"></a> Quick description\n\n_Interactive clustering_ is a method intended to assist in the design of a training data set.\n\nThis iterative process begins with an unlabeled dataset, and it uses a sequence of two substeps :\n\n1. the user defines constraints on data sampled by the computer ;\n\n2. the computer performs data partitioning using a constrained clustering algorithm.\n\nThus, at each step of the process :\n\n- the user corrects the clustering of the previous steps using constraints, and\n\n- the computer offers a corrected and more relevant data partitioning for the next step.\n\nThe process use severals objects :\n\n- 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) ;\n\n- a _constraints sampler_ : its role is to select the most relevant data during the annotation of constraints by the user ;\n\n- a _constrained clustering algorithm_ : its role is to partition the data while respecting the constraints provided by the user.\n\n_NB_ :\n\n- This python library does not contain integration into a graphic interface.\n\n- For more details, read the [Documentation](#Documentation) and the articles in the [References](#References) section.\n\n\n## <a name=\"Documentation\"></a> Documentation\n\n- [Main documentation](https://cognitivefactory.github.io/interactive-clustering/)\n\n\n## <a name=\"Installation\"></a> Installation\n\nInteractive Clustering requires Python 3.8 or above.\n\nTo install with [`pip`](https://github.com/pypa/pip):\n\n```bash\n# install package\npython3 -m pip install cognitivefactory-interactive-clustering\n\n# install spacy language model dependencies (the one you want, with version \"3.4.x\")\npython3 -m spacy download fr_core_news_md-3.4.0 --direct\n```\n\nTo install with [`pipx`](https://github.com/pypa/pipx):\n\n```bash\n# install pipx\npython3 -m pip install --user pipx\n\n# install package\npipx install --python python3 cognitivefactory-interactive-clustering\n\n# install spacy language model dependencies (the one you want, with version \"3.4.x\")\npython3 -m spacy download fr_core_news_md-3.4.0 --direct\n```\n\n_NB_ : Other spaCy language models can be downloaded here : [spaCy - Models & Languages](https://spacy.io/usage/models). Use spacy version `\"3.4.x\"`.\n\n\n## <a name=\"Development\"></a> Development\n\nTo work on this project or contribute to it, please read:\n\n- the [Copier PDM](https://pawamoy.github.io/copier-pdm/) template documentation ;\n- the [Contributing](https://cognitivefactory.github.io/interactive-clustering/contributing/) page for environment setup and development help ;\n- the [Code of Conduct](https://cognitivefactory.github.io/interactive-clustering/code_of_conduct/) page for contribution rules.\n\n\n## <a name=\"References\"></a> References\n\n- **Interactive Clustering**:\n\t- PhD report: `Schild, E. (2024, in press). De l'Importance de Valoriser l'Expertise Humaine dans l'Annotation : Application \u00e0 la Mod\u00e9lisation de Textes en Intentions \u00e0 l'aide d'un Clustering Interactif. Universit\u00e9 de Lorraine.` ;\n\t- First presentation: `Schild, E., Durantin, G., Lamirel, J.C., & Miconi, F. (2021). Conception it\u00e9rative et semi-supervis\u00e9e d'assistants conversationnels par regroupement interactif des questions. In EGC 2021 - 21\u00e8mes Journ\u00e9es Francophones Extraction et Gestion des Connaissances. Edition RNTI. <hal-03133007>.`\n\t- 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>.`\n\t- Methodological discussion: `Schild, E., Durantin, G., & Lamirel, J.C. (2021). Concevoir un assistant conversationnel de mani\u00e8re it\u00e9rative et semi-supervis\u00e9e avec le clustering interactif. In Atelier - Fouille de Textes - Text Mine 2021 - En conjonction avec EGC 2021. <hal-03133060>.`\n\n- **Constraints and Constrained Clustering**:\n\t- Constraints in clustering: `Wagstaff, K. et C. Cardie (2000). Clustering with Instance-level Constraints. Proceedings of the Seventeenth International Conference on Machine Learning, 1103\u20131110.`\n\t- 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\u20131707.`\n\t- Affinity Propagation:\n\t\t- Affinity Propagation Clustering: `Frey, B. J., & Dueck, D. (2007). Clustering by Passing Messages Between Data Points. In Science (Vol. 315, Issue 5814, pp. 972\u2013976). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.1136800`\n\t\t- 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`\n\t- DBScan:\n\t\t- DBScan Clustering: `Ester, Martin & Kr\u00f6ger, Peer & Sander, Joerg & Xu, Xiaowei. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD. 96. 226-231`.\n\t\t- 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.`\n\t- KMeans Clustering:\n\t\t- 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\u2013297.`\n\t\t- 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`\n\t\t- 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\u201324). Scientific Research Publishing, Inc. https://doi.org/10.4236/wsn.2012.41003`\n\t- Hierarchical Clustering:\n\t\t- Hierarchical Clustering: `Murtagh, F. et P. Contreras (2012). Algorithms for hierarchical clustering : An overview. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 2, 86\u201397.`\n\t\t- Constrained Hierarchical Clustering: `Davidson, I. et S. S. Ravi (2005). Agglomerative Hierarchical Clustering with Constraints : Theoretical and Empirical Results. Springer, Berlin, Heidelberg 3721, 12.`\n\t- Spectral Clustering:\n\t\t- 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.`\n\t\t- 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\u2013566.`\n\n- **Preprocessing and Vectorization**:\n\t- _spaCy_: `Honnibal, M. et I. Montani (2017). spaCy 2 : Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing.`\n\t\t- _spaCy_ language models: `https://spacy.io/usage/models`\n\t- _NLTK_: `Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O\u2019Reilly Media Inc.`\n\t\t- _NLTK_ _'SnowballStemmer'_: `https://www.nltk.org/api/nltk.stem.html#module-nltk.stem.snowball`\n\t- _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\u20132830.`\n\t\t- _Scikit-learn_ _'TfidfVectorizer'_: `https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html`\n\n\n## <a name=\"Other links\"></a> Other links\n\n- 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`\n- 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`\n\n\n## <a name=\"How to cite\"></a> How to cite\n\n`Schild, E. (2021). cognitivefactory/interactive-clustering. Zenodo. https://doi.org/10.5281/zenodo.4775251.`\n\n",
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