2018/09/11 by Dmitry Morozov, Morozov, Dmitry, Mario Lezoche +3
Computer Science · Engineering · #Algorithm #Artificial intelligence #Business process #Cluster analysis #Computer science #Data Mining Algorithms and Applications #Data mining #Data science #Electroencephalography #Engineering #Event (particle physics) #Extension (predicate logic) #FOS: Computer and information sciences #FOS: Electrical engineering #Formal concept analysis #Information Retrieval (cs.IR) #Knowledge extraction #Natural language processing #Neural Networks and Applications #Process (computing) #Process mining #Programming language #Reflection (computer programming) #Representation (politics) #Rough Sets and Fuzzy Logic #Signal Processing (eess.SP) #Work in process #cs.IR #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1809.09955
arXiv admin note: text overlap with arXiv:1506.05018 by other authors
arxiv created 2018/09/11 · openalex publication_date 2018/09/11 · arxiv updated 2018/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Formal Concept Analysis (FCA) is a well-established method for data analysis which finds many applications in data mining. Its extension on complex data representation formats brought a wave of new applications to the problems such as gene expression mining, prediction of toxicity of chemical compounds or clustering of sequences in process event logs. Insipired from this work our research inherits their model and designs an experiment for mining electroencephalographic recordings for patterns of sleep spindles. The contribution of this paper lies in the specification of desritizition procedure and the architecture of FCA experiment. We also provide some reflection on the related research papers.