Skip to Main content Skip to Navigation

Exploring sequential data with relational concept analysis

Abstract : Many sequential pattern mining methods have been proposed to discover useful patterns that describe the analysed sequential data. Several of these works have focused on efficiently enumerating all closed partially-ordered patterns (cpo-patterns), that makes their evaluation a laboured task for experts since their number can be large. To address this issue, we propose a new approach, that is to directly extract multilevel cpo-patterns implicitly organised into a hierarchy. To this end, we devise an original method within the Relational Concept Analysis (RCA) framework, referred to as RCA-SEQ, that exploits the structure and properties of the lattices from the RCA output. RCA-SEQ spans five steps: the preprocessing of the raw data; the RCA-based exploration of the preprocessed data; the automatic extraction of a hierarchy of multilevel cpo-patterns by navigating the lattices from the RCA output; the selection of relevant multilevel cpo-patterns; the pattern evaluation done by experts.
Document type :
Complete list of metadatas
Contributor : Abes Star :  Contact
Submitted on : Monday, February 19, 2018 - 3:46:06 PM
Last modification on : Friday, May 17, 2019 - 11:37:57 AM
Long-term archiving on: : Monday, May 7, 2018 - 7:49:49 AM


Version validated by the jury (STAR)


  • HAL Id : tel-01712510, version 1


Cristina Nica. Exploring sequential data with relational concept analysis. Artificial Intelligence [cs.AI]. Université de Strasbourg, 2017. English. ⟨NNT : 2017STRAD032⟩. ⟨tel-01712510⟩



Record views


Files downloads