A comparative study of two optimization clustering techniques on unemployment data

• Author(s): Barros, Elisa ; Nunes, Alcina ; Balsa, Carlos
• Date: 2013
• Persistent ID: http://hdl.handle.net/10198/10382
• Origin: Biblioteca Digital do IPB
• Subject(s): Clustering methods; K-means; Spectral clustering; Unemployment data mining
• Document Type: Article
• Language: Portuguese

Description

An important strategy for data classi cation 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.