Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering
Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the applicat...
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| Outros Autores: | , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2007
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10316/5042 |
| País: | Portugal |
| Oai: | oai:estudogeral.sib.uc.pt:10316/5042 |
| Resumo: | Techniques based on agglomerative hierarchical clustering constitute one of the most frequent approaches in unsupervised clustering. Some are based on the single linkage methodology, which has been shown to produce good results with sets of clusters of various sizes and shapes. However, the application of this type of algorithms in a wide variety of fields has posed a number of problems, such as the sensitivity to outliers and fluctuations in the density of data points. Additionally, these algorithms do not usually allow for automatic clustering. |
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