The EU recently proposed a single trade agreement with the United States, so there is no reason it cannot have one with the UK post-Brexit, according to the chief executive of the UK’s Financial Conduct Authority (FCA)
conexp-ng - ConExp-NG is a simple GUI-centric tool for the study & research of Formal Concept Analysis (FCA) that allows you to create formal contexts, draw concept lattices and explore dependencies between attributes.
CLA is an international conference dedicated to formal concept analysis (FCA) and areas closely related to FCA such as data mining, information retrieval, knowledge management, data and knowledge engineering, logic, algebra and lattice theory. CLA provides a forum for researchers, practitioners, and students. The program of CLA consists of invited plenary talks, regular talks, and poster sessions. Papers in all areas relevant to theory and applications of FCA are solicited.
BACKGROUND: Research confirms that physical activity (PA) is irreplaceable in a healthy and physically active lifestyle. One of the key research questions is what the optimal level of everyday PA for health is and how it should be quantified and interpreted. Formal concept analysis is one possible way of how to assess and describe the level of PA as related to personal data. OBJECTIVE: The main goal of this study was to introduce the method of Formal Concept Analysis (FCA) using data from the ANEWS questionnaire and data from the objective monitoring of a number of steps using the YAMAX SW-701 pedometer. A further aim was to adopt the most appropriate method within the FCA. METHODS: A random sample of 273 males aged 18-69 from selected regional centers participated in the study. RESULTS: The example of daily steps allows for the demonstration of how important it is to select a scale in FCA data analysis. It is better to use an ordinal scale for the daily number of steps (in our example); because, in so doing, we create the attributes that can be ordered (a lower number of steps is also insufficient). CONCLUSIONS: A rough scale produces easier lattice with the general scope of the observed attributes. The smoothing of the scale produces more difficult lattice and makes for more difficult analyses, but gives more detailed results. FCA requires more expertise from a researcher than do "classical" testing statistics, but gives us deeper insight into the examination of the problem.