Companies’ objectives extend beyond mere profitability, to what is generally known as Corporate Social Responsibility (CSR). Empirical research effort of CSR is typically concentrated on a limited number of aspects. We focus on the whole set of CSR activities to identify any structure to that set. In this analysis, we take data from 1850 of the largest international companies via the conventional MSCI database and focus on four major dimensions of CSR: Environment, Social/Stakeholder, Labor, and Governance. To identify any structure hidden in almost constant average values, we apply the popular technique of K-means clustering. When determining the number of clusters, which is especially difficult in the case at hand, we use an equivalent clustering criterion that is complementary to the square-error K-means criterion. Our use of this complementary criterion aims at obtaining clusters that are both large and farthest away from the center. We derive from this a method of extracting anomalous clusters one-by-one with a follow-up removal of small clusters. This method has allowed us to discover a rather impressive process of change from predominantly uniform patterns of CSR activities along the four dimensions in 2007 to predominantly single-focus patterns of CSR activities in 2012. This change may reflect the dynamics of increasingly interweaving and structuring CSR activities into business processes that are likely to be extended into the future.

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We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a domain taxonomy. This generalization lifts the set to its "head subject" node in the higher ranks of the taxonomy tree. The head subject is supposed to "tightly" cover the query set, possibly bringing in some errors referred to as "gaps" and "offshoots". Our method, ParGenFS, globally minimizes a penalty function combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from the celebrated ACM Computing Classification System 2012. Based on a collection of research papers published by Springer 1998-2017, and applying in-house methods for text analysis and fuzzy clustering, we derive fuzzy clusters of leaf topics in learning, retrieval and clustering. The head subjects of these clusters inform us of some general tendencies of the research. The latter involves publicly available IAB Tech Lab Content Taxonomy. Each of about 25 mln users is assigned with a fuzzy profile within this taxonomy, which is generalized offline using ParGenFS. Our experiments show that these head subjects effectively extend the size of targeted audiences at least twice without loosing quality.