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| Paper: |
Knowledge Discovery Workflows in the Exploration of Complex Astronomical Datasets |
| Volume: |
461, Astronomical Data Analysis Software and Systems XXI |
| Page: |
485 |
| Authors: |
D'Abrusco, R.; Fabbiano, G.; Laurino, O.; Longo, G. |
| Abstract: |
In this paper we present the Clustering-Labels-Score Patterns Spotter (CLaSPS), a new
methodology for the determination of correlations among astronomical observables in
complex datasets, based on the application of distinct unsupervised clustering techniques
and the use of additional information for the selection of the optimal spontaneous associations
of sources in the original feature space. The novelty in this approach is the criterion
followed for the selection of the optimal clusterings, based on a quantitative measure of the
degree of correlation between the features used for the determination of the clusters
and a set of observables, the labels, not employed for the clustering. |
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