We review the current state of data mining and machine learning in astronomy. Data Mining
can have a somewhat mixed connotation from the point of view of a researcher in this field. If …

Most existing star-galaxy classifiers use the reduced summary information from catalogs,
requiring careful feature extraction and selection. The latest advances in machine learning …

With the coming data deluge from synoptic surveys, there is a need for frameworks that can
quickly and automatically produce calibrated classification probabilities for newly observed …

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is
not an exception and is now facing a deluge of data. For galaxies, the one-century old …

This paper explores the application of machine learning methods for classifying
astronomical sources using photometric data, including normal and emission line galaxies …

We describe a technique for simultaneously classifying and estimating the redshift of
quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior …

We present the results of a proof-of-concept experiment that demonstrates that deep
learning can successfully be used for production-scale classification of compact star clusters …

In modern astrophysics, machine learning has increasingly gained popularity with its
incredibly powerful ability to make predictions or calculated suggestions for large amounts of …

We conduct a pilot investigation to determine the optimal combination of color and variability
information to identify quasars in current and future multi-epoch optical surveys. We use a …

We provide classifications for all 143 million nonrepeat photometric objects in the Third Data
Release of the SDSS using decision trees trained on 477,068 objects with SDSS …