On Irrelevance of Attributes in Flexible Prediction
  This paper analyses properties of conceptual hierarchy obtained via
incremental concept formation method called "flexible prediction" in order to
determine what kind of "relevance" of participating attributes may be requested
for meaningful conceptual hierarchy. The impact of selection of simple and
combined attributes, of scaling and of distribution of individual attributes
and of correlation strengths among them is investigated. Paradoxically, both:
attributes weakly and strongly related with other attributes have deteriorating
impact onto the overall classification. Proper construction of derived
attributes as well as selection of scaling of individual attributes strongly
influences the obtained concept hierarchy. Attribute density of distribution
seems to influence the classification weakly
  It seems also, that concept hierarchies (taxonomies) reflect a compromise
between the data and our interests in some objective truth about the data. To
obtain classifications more suitable for one's purposes, breaking the symmetry
among attributes (by dividing them into dependent and independent and applying
differing evaluation formulas for their contribution) is suggested. Both
continuous and discrete variables are considered. Some methodologies for the
former are considered.
