A comparative study of two optimization clustering techniques on unemployment data
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abstract
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.
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.
