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.
This study explores the potential of the innovation modes, a firm-level taxonomy of innovation behavior, to provide a reasonable treatment for the growing complexity and multidimensionality of company strategies, incentives, and demands. The data on the Russian manufacturing enterprises from two complementary surveys are used to estimate broader features of the firms pursuing particular innovation modes, including the intensity, efficiency, and impact of innovation activities, the importance of factors, hampering the performance and the heterogeneity of demand for the policy support measures. Resulting composition of the firm-level patterns and characteristics brings new facilities for the diagnosis-based policy-making in the field of innovation.