We describe a novel method for the analysis of research activities of an organization by mapping that to a taxonomy tree of the field. The method constructs fuzzy membership profiles of the organizationmembers or teams in terms of the taxonomy's leaves (research topics), and then it generalizes them in two steps. These steps are: (i) fuzzy clustering research topics according to their thematic similarities in the department, ignoring the topology of the taxonomy, and (ii) optimally lifting clusters mapped to the taxonomy tree to higher ranked categories by ignoring "small" discrepancies. We illustrate the method by applying it to data collected by using an in-house e-survey tool from a university department and from a university research center. The method can be considered for knowledge generalization over any taxonomy tree.
|Title of host publication||Advances in Reasoning-Based Image Processing Intelligent Systems: Conventional and Intelligent Paradigms|
|Editors||Roumen Kountchev, Kazumi Nakamatsu|
|Place of Publication||Berlin Heidelberg|
|Publication status||Published - 1 Jan 2012|
|Name||Series on Intelligent Systems Reference Library|