Abstract
Developing both structured and unstructured representations, including explicit versus implicit knowledge, as well as white-box versus black-box approaches is a richness for Knowledge Discovery (KD). Formal Concept Analysis (FCA) is positioned at the spectrum end which focuses on complex data, structured representations, explicit knowledge, explainable results and white-box approaches. More and more connections are also made with other methods in the whole KD spectrum. Relational Concept Analysis (RCA) extends FCA by highlighting knowledge in multi-relational datasets. It has a variety of applications, in particular in Software Engineering and Environmental data. As a KD method, careful and adequate data modeling and preparation are crucial for a successful application. Experience gained during past theoretical or experimental studies can be capitalized in design patterns, by identifying and collecting different recurring situations and adopted solutions. Some of them can be used in other KD methods. This talk presents the principles of RCA, its specific value for KD and its tooling. It also situates RCA among the FCA-based KD methods. Then it develops a tentative design pattern catalog, while connecting these design patterns to concrete applications. By building this catalog, we aim to facilitate RCA practice, save time and increase quality in future applications and to consolidate an efficient KD method.