Stereotype Extraction with Default Clustering

Velcin, Julien; Ganascia, Jean-Gabriel

Stereotype Extraction with Default Clustering

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Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 883-887.

Abstract

The concept of stereotype seems to be really adapted when wishing to extract meaningful descriptions from data, especially when there is a high rate of missing values. This paper proposes a logical framework called default clustering based on default reasoning and local search techniques. The first experiment deals with the rediscovering of initial descriptions from artificial data sets, the second one extracts stereotypes of politicians in a real case generated from newspaper articles. It is shown that default clustering is more adapted in this context than the three classical clusterers considered.

Julien Velcin, and Jean-Gabriel Ganascia. 2005. Stereotype Extraction with Default Clustering. In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05. IJCAI, 883–887. https://ijcai.org/proceedings/2005/113

@inproceedings{ijcai2005p113,
  title     = {Stereotype Extraction with Default Clustering},
  author    = {Velcin, Julien and Ganascia, Jean-Gabriel},
  booktitle = {Proceedings of the Nineteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-05}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  pages     = {883--887},
  year      = {2005},
  url       = {https://ijcai.org/proceedings/2005/113},
}

TY  - CPAPER
TI  - Stereotype Extraction with Default Clustering
AU  - Velcin, Julien
AU  - Ganascia, Jean-Gabriel
T2  - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05
PB  - International Joint Conferences on Artificial Intelligence
PY  - 2005
SP  - 883
EP  - 887
UR  - https://ijcai.org/proceedings/2005/113
ER  -