  One of the goals of machine learning is to eliminate tedious and arduous
repetitive work. The manual and semi-automatic classification of millions of
hours of solar wind data from multiple missions can be replaced by automatic
algorithms that can discover, in mountains of multi-dimensional data, the real
differences in the solar wind properties. In this paper we present how
unsupervised clustering techniques can be used to segregate different types of
solar wind. We propose the use of advanced data reduction methods to
pre-process the data, and we introduce the use of Self-Organizing Maps to
visualize and interpret 14 years of ACE data. Finally, we show how these
techniques can potentially be used to uncover hidden information, and how they
compare with previous manual and automatic categorizations.
