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 …

Many classification problems must deal with imbalanced datasets where one class–the
majority class–outnumbers the other classes. Standard classification methods do not …

We present a general probabilistic formalism for cross-identifying astronomical point sources
in multiple observations. Our Bayesian approach, symmetric in all observations, is the …

We examine the H i-to-stellar mass ratio (H i fraction) for galaxies near filament backbones
within the nearby Universe (d< 181 Mpc). This work uses the 6-degree Field Galaxy Survey …

References............................................................................ 1095.1 Introduction It has been said
that astronomers have been doing data mining for centuries:“thedata are mine, and you …

We compare the performance of two automated classification algorithms, k-dimensional tree
(kd-tree) and support vector machines (SVMs), to separate quasars from stars in the data …

Identifying the infrared counterparts of X-ray sources in Galactic plane fields such as those of
the MYStIX project presents particular difficulties due to the high density of infrared sources …

The emission-lines of galaxies originate from massive young stars or supermassive
blackholes. As a result, spectral classification of emission-line galaxies into star-forming …

One-to-many data linkage is an essential task in many domains, yet only a handful of prior
publications have addressed this issue. Furthermore, while traditionally data linkage is …

Several studies have reported that the ensemble of classifiers can improve the performance
of a stand-alone classifier. In this paper, we propose a learning method for combining the …