Revealing conceptual structure in data by inductive inference

ABSTRACTIn many applied sciences there is often a problem of revealing a structure underlyinga given collection of objects (situations, measurements, observations, etc.).A specific problem of this type is that of determining a hierarchy of meaningfulsubcategories in such a collection. This problem has been studied intensively inthe area of cluster analysis. The methods developed there, however, formulatesubcategories ('clusters') solely on the basis of pairwise 'similarity' (or 'proximity')of objects, and ignore the issue of the 'meaning' of the clusters obtained. Themethods do not provide any description of the clusters obtained. This paperpresents a method which constructs a hierarchy of subcategories, such that anappropriately generalized description of each subcategory is a single conjunctivestatement involving attributes of objects and has a simple conceptual interpretation.In Hayes, J. E., Michie, D., and Pao, Y.-H. (Eds.), Machine Intelligence 10. Ellis Horwood.