Решение общей проблемы информационного взрыва связано с системами
автоматической обработки цифровых данных, включая их распознавание, сортировку …

Formal concepts and closed itemsets proved to be of big importance for knowledge
discovery, both as a tool for concise representation of association rules and a tool for …

Subgroup discovery is the task of discovering patterns that accurately discriminate a class
label from the others. Existing approaches can uncover such patterns either through an …

Analyzing and understanding large and complex volumes of biological data is a challenging
task as data becomes more widely available. In biomedical research, gene expression data …

The article explores the construction of semantic models of subject domains to formalize the
existing knowledge about the subject domain and generate new knowledge. Methods of …

In this paper, we propose a hybrid and exploratory knowledge discovery approach for
analyzing metabolomic complex data based on a combination of supervised classifiers …

Mining important conceptual patterns is an essential task for understanding the context and
content of complex data in many scientific and engineering applications. While exact …

A scalable method for mining graph patterns stable under subsampling is proposed. The
existing subsample stability and robustness measures are not antimonotonic according to …

Abstract Knowledge Discovery in Databases (KDD) and especially pattern mining can be
interpreted along several dimensions, namely data, knowledge, problem-solving and …

Formal Concept Analysis can be considered as a classification engine able to build classes
of objects with a description or concepts and to organize these concepts within a concept …