Default Clustering from Sparse Data Sets - Archive ouverte HAL Access content directly
Conference Papers Year : 2005

Default Clustering from Sparse Data Sets


Categorization with a very high missing data rate is seldom studied, especially from a non-probabilistic point of view. This paper proposes a new algorithm called default clustering that relies on default reasoning and uses the local search paradigm. Two kinds of experiments are considered: the first one presents the results obtained on artificial data sets, the second uses an original and real case where political stereotypes are extracted from newspaper articles at the end of the 19th century.

Dates and versions

hal-01490510 , version 1 (15-03-2017)



Julien Velcin, Jean-Gabriel Ganascia. Default Clustering from Sparse Data Sets. ECSQARU 2005 - 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Jul 2005, Barcelona, Spain. pp.968-979, ⟨10.1007/11518655_81⟩. ⟨hal-01490510⟩
52 View
0 Download



Gmail Facebook X LinkedIn More