A model for clustering data from heterogeneous dissimilarities
Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many applications, mor...
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| Principais autores: | , , |
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| Formato: | article |
| Idioma: | English |
| Publicado em: |
Elsevier
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/30633 |
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