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14

ECSQARU
2005
Springer

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Default Clustering from Sparse Data Sets

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Default Clustering from Sparse Data Sets

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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.

Julien Velcin, Jean-Gabriel Ganascia

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Added	27 Jun 2010
Updated	27 Jun 2010
Type	Conference
Year	2005
Where	ECSQARU
Authors	Julien Velcin, Jean-Gabriel Ganascia

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