Abstract — We present a method for mapping a structure to a related taxonomy in a thematically consistent way. The components of the structure are supplied with fuzzy profiles over the taxonomy. These are then generalized in two steps: first, by fuzzy clustering, and then by mapping the clusters to higher ranks of the taxonomy. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is aplicable also to other taxonomies. We build fuzzy clusters of the taxonomy subjects according to the similarity between individual profiles. Clusters are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions. The clusters are parsimoniously lifted to higher ranks of the taxonomy using an original recursive algorithm for minimizing a penalty function that involves “head subjects ” on the higher ranks of the taxonomy along with their “gaps ” and “offshoots”. An example is given illustrating the method applied to real-world data. I.

Abstract. A hybrid of two novel methods- additive fuzzy spectral clustering and lifting method over a taxonomy- is applied to analyse the research activities of a department. To be specific, we concentrate on the Computer Sciences area represented by the ACM Computing Classification System (ACM-CCS), but the approach is applicable also to other taxonomies. Clusters of the taxonomy subjects are extracted using an original additive spectral clustering method involving a number of model-based stopping conditions. The clusters are parsimoniously lifted then to higher ranks of the taxonomy by minimizing the count of “head subjects ” along with their “gaps ” and “offshoots”. An example is given illustrating the method applied to real-world data. 1