  We present variability analysis of data from the Northern Sky Variability
Survey (NSVS). Using the clustering method which defines variable candidates as
outliers from large clusters, we cluster 16,189,040 light curves, having data
points at more than 15 epochs, as variable and non-variable candidates in 638
NSVS fields. Variable candidates are selected depending on how strongly they
are separated from the largest cluster and how rarely they are grouped together
in eight dimensional space spanned by variability indices. All NSVS light
curves are also cross-correlated to the Infrared Astronomical Satellite, AKARI,
Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and Galaxy
Evolution Explorer objects as well as known objects in the SIMBAD database. The
variability analysis and cross-correlation results are provided in a public
online database which can be used to select interesting objects for further
investigation. Adopting conservative selection criteria for variable
candidates, we find about 1.8 million light curves as possible variable
candidates in the NSVS data, corresponding to about 10% of our entire NSVS
samples. Multi-wavelength colors help us find specific types of variability
among the variable candidates. Moreover, we also use morphological
classification from other surveys such as SDSS to suppress spurious cases
caused by blending objects or extended sources due to the low angular
resolution of the NSVS.
