Formal Concept Analysis for Knowledge Discovery. Proceedings of International Workshop on Formal Concept Analysis for Knowledge Discovery (FCA4KD 2017), Moscow, Russia, June 1, 2017.
Symbolic classifiers allow for solving classification task and provide the reason for the classifier decision. Such classifiers were studied by a large number of researchers and known under a number of names including tests, JSM-hypotheses, version spaces, emerging patterns, proper predictors of a target class, representative sets etc. Here we consider such classifiers with restriction on counter-examples and discuss them in terms of pattern structures. We show how such classifiers are related. In particular, we discuss the equivalence between good maximally redundant tests and minimal JSM-hyposethes and between minimal representations of version spaces and good irredundant tests.
The importance of weak social ties in professional commu- nities is well studied and widely accepted. In our paper we analyze the structure of strong ties based on the co-authorship relation and use the formal concept analysis framework to figure out weak ties. The research is motivated by fast growing need in cross-disciplinary research, which re- quires experts from different areas to understand the bigger picture and identify potential fellows for collaborative research projects in nearest future.
Today personalized medicine is one of the most popular interdisciplinary research field, risk group identification being one of its most important tasks. Even though the first attempts to estimate the effect of patient’s characteristics on the outcome were proposed in statistics in the middle of the twentieth century, it is still an open question how to explore such effects properly. In this paper we propose a trial version of the approach to risk group specification based on pattern structures and competing risk estimation, and discuss further steps of research on its performance and specificity.