Abstract
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, concept hierarchy of attributes rather than of objects should be looked for. A proposal of bayesian network based clustering is proposed.
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Klopotek, M.A. (2001). Taxonomy Building: Cases or Attributes ?. In: Kłopotek, M.A., Michalewicz, M., Wierzchoń, S.T. (eds) Intelligent Information Systems 2001. Advances in Intelligent and Soft Computing, vol 10. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1813-0_9
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DOI: https://doi.org/10.1007/978-3-7908-1813-0_9
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