We are living in the era of large catalogs. Everyday many observatories, terrestrial and space-based, provide us with numerous data to do science. In this context, statistical analysis tools for large data sets (such as Machine Learnign) play a fundamental role. An example of such a survey is the SLOAN Moving Object Catalog (MOC), which lists astrometric and photometric information of all moving objects captured by the SLOAN field of view. A great advantage of this telescope is its set of 5 filters that allows 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 unaffected by the magnitude variations caused by the change in phase angle. We selected 9481 asteroids with absolute magnitudes Hg, Hi and Hz, computed from the SLOAN Moving Objects Catalog using the HG^*^_12_ system, and calculated absolute colors with them. To perform the taxonomic classification, we applied a non-supervised Machine Learning algorithm known as fuzzy C-means. This is a useful soft clustering tool for working with overlapping data. We have decided 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. 162 of these objects had an ambiguous classification in the past. By analyzing the sample obtained in the plane Semi-major axis vs. inclination, we identified 15 new V-type asteroid candidates outside the Vesta family region.