An unsupervised learning system does not require preclassified examples. Conceptual clustering is an instance of unsupervised learning, which discovers useful categories in unclassified data.
Conceptual clustering is well-matched to many problems in astronomy.
This is a sub-problem of the more general task of morphological classification of sky objects. Machine learning techniques in general tend to have the advantage over humans of being able to handle large amounts of data of large dimensionality. Unsupervised learning has the advantage over supervised learning that the subjective element introduced by the human is eliminated, leaving only a clearly defined and objective process. In addition, a good unsupervised learning scheme has the ability to identify new classes that were previously unknown to the human user. Various unsupervised learning methodologies have been applied to this problem, to be discussed in later reports.