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 firs...
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| creator | Velcin, J. Ganascia, J. -G. |
| description | 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. |
| doi_str_mv | 10.1007/11518655_81 |
| format | Conference Proceeding |
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| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | ECSQARU 2005 - 8th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005, Vol.3571, p.968-979 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_16991391 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer Science Computer science control theory systems Conceptual Cluster Default Rule Exact sciences and technology Local Search Relative Cover Sparse Data |
| title | Default Clustering from Sparse Data Sets |
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