Distilling Conceptual Structures from Weblog Data Using Polyadic FCA

  • Sanda-Maria DragoşEmail author
  • Diana-Florina Haliţă
  • Christian Săcărea
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9717)


Formal Concept Analysis (FCA) is a prominent field of applied mathematics which is closely related to knowledge discovery, processing and representation. We consider the problem of distilling relevant conceptual structures from weblog data, more precisely, we investigate users’ behavioral patterns in an web based educational platform by using n-adic FCA (\(n=3, n=4\)). We focus in our research on log data gathered from e-learning platforms. Such systems are particularly interesting, since user’s behavioral patterns are closely related to their academic performance. We investigate user’s behavior by using similarity measures of various visited page chains. We exemplify the methods we have developed on a locally developed e-learning platform called PULSE. Data gathered from weblogs have been preprocessed and conceptual landscapes of knowledge have been built using FCA. Triadic FCA (3FCA) is used to investigate correlations between similar page chains and the time granule when a certain pattern occurs. Finally, we employ tetradic FCA (4FCA) to compare web usage patterns wrt. temporal development and occurence. As far as we know, this is the first attempt to use 4FCA in web usage mining.


Web usage mining Behavioral patterns Formal Concept Analysis Similarity measures 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sanda-Maria Dragoş
    • 1
    Email author
  • Diana-Florina Haliţă
    • 1
  • Christian Săcărea
    • 1
  1. 1.Department of Computer ScienceBabeş-Bolyai UniversityCluj-NapocaRomania

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