CLaSPS: a new methodology for Knowledge extraction from complex astronomical dataset
R. D'Abrusco, G. Fabbiano, G. Djorgovski, C. Donalek, O. Laurino, G., Longo

TL;DR
CLaSPS is a novel unsupervised clustering methodology that identifies correlations in complex astronomical datasets by evaluating the relationship between cluster memberships and observable labels, demonstrated on quasar and blazar datasets.
Contribution
The paper introduces CLaSPS, a new clustering-based method for discovering correlations in astronomical data using a novel label-cluster correlation criterion.
Findings
Re-identified known correlation between alphaOX and UV color in quasars.
Discovered a strong link between blazar colors and optical spectral types.
Identified a unique pattern in blazar mid-infrared colors with physical implications.
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. The novelty in CLaSPS is the criterion used for the selection of the optimal clusterings, based on a quantitative measure of the degree of correlation between the cluster memberships and the distribution of a set of observables, the labels, not employed for the clustering. In this paper we discuss the applications of CLaSPS to two simple astronomical datasets, both composed of extragalactic sources with photometric observations at different wavelengths from large area surveys. The first dataset, CSC+, is composed of optical quasars spectroscopically selected in the SDSS data, observed in the X-rays by Chandra and with…
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