Publicação
A comparative study of two optimization clustering techniques on unemployment data
| Resumo: | An important strategy for data classification consists in organising data points in clusters. The $k$-means is a traditional optimisation method applied to cluster data points. Using a labour market database, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means. |
|---|---|
| Autores principais: | Barros, Elisa |
| Outros Autores: | Nunes, Alcina; Balsa, Carlos |
| Assunto: | Clustering methods k-means Spectral clustering Unemployment data mining |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | português |
| Origem: | Biblioteca Digital do IPB |
| Resumo: | An important strategy for data classification consists in organising data points in clusters. The $k$-means is a traditional optimisation method applied to cluster data points. Using a labour market database, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means. |
|---|