  We are in an era of large catalogs and, thus, statistical analysis tools for
large data sets, such as machine learning, play a fundamental role. One example
of such a survey is the Sloan Moving Object Catalog (MOC), which lists the
astrometric and photometric information of all moving objects captured by the
Sloan field of view. One great advantage of this telescope is represented by
its set of five filters, allowing for taxonomic analysis of asteroids by
studying their colors. However, until now, the color variation produced by the
change of phase angle of the object has not been taken into account. In this
paper, we address this issue by using absolute magnitudes for classification.
We aim to produce a new taxonomic classification of asteroids based on their
magnitudes that is unaffected by variations caused by the change in phase
angle. We selected 9481 asteroids with absolute magnitudes of Hg, Hi and Hz,
computed from the Sloan Moving Objects Catalog using the HG12 system. We
calculated the absolute colors with them. To perform the taxonomic
classification, we applied a unsupervised machine learning algorithm known as
fuzzy C-means. This is a useful soft clustering tool for working with {data
sets where the different groups are not completely separated and there are
regions of overlap between them. We have chosen to work with the four main
taxonomic complexes, C, S, X, and V, as they comprise most of the known
spectral characteristics. We classified a total of 6329 asteroids with more
than 60% probability of belonging to the assigned taxonomic class, with 162 of
these objects having been characterized by an ambiguous classification in the
past. By analyzing the sample obtained in the plane Semimajor axis versus
inclination, we identified 15 new V-type asteroid candidates outside the Vesta
family region.
