Unsupervised Classification Procedures Applied to Satellite Cloud Data

Machine learning algorithms can be subdivided into two types, supervised and unsupervised. Supervised learning is the more useful technique when the data samples have known outcomes that the user wants to predict. On the other hand, unsupervised learning is more appropriate when the user does not know the subdivisions into which the data samples, using relevant predictor features, should be divided. Prior categorical division may not be obvious because the problem may be a new one, for which the user has little experience. In such a case, an unsupervised learning procedure can provide insight into groupings that may make physical sense and facilitate future analysis. In this report, we explore the potential of two unsupervised learning programs, AutoClass and K-Means, when applied to a data set that was developed from satellite imagery of cloud regions that were expertly labeled into ten classes. Because cloud types hold meteorological significance, an automated classification from satellite imagery is of obvious use. We compare cloud classes produced by these systems with traditional cloud classes.