  During the last ten years, a considerable amount of effort has been made to
develop algorithms for automatic classification of variable stars. That has
been primarily achieved by applying machine learning methods to photometric
datasets where objects are represented as light curves. Classifiers require
training sets to learn the underlying patterns that allow the separation among
classes. Unfortunately, building training sets is an expensive process that
demands a lot of human efforts. Every time data comes from new surveys; the
only available training instances are the ones that have a cross-match with
previously labelled objects, consequently generating insufficient training sets
compared with the large amounts of unlabelled sources. In this work, we present
an algorithm that performs unsupervised classification of variable stars,
relying only on the similarity among light curves. We tackle the unsupervised
classification problem by proposing an untraditional approach. Instead of
trying to match classes of stars with clusters found by a clustering algorithm,
we propose a query based method where astronomers can find groups of variable
stars ranked by similarity. We also develop a fast similarity function specific
for light curves, based on a novel data structure that allows scaling the
search over the entire dataset of unlabelled objects. Experiments show that our
unsupervised model achieves high accuracy in the classification of different
types of variable stars and that the proposed algorithm scales up to massive
amounts of light curves.
