Extending Redescription Mining to Multiple Views

  • Matej MihelčićEmail author
  • Sašo Džeroski
  • Tomislav Šmuc
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11198)


Redescription mining is a data mining task that discovers re-descriptions of different subsets of entities from available data. Locating such re-descriptions is important in many scientific disciplines because it allows detecting different types of associations including synergy of different attributes of interest. There exist a number of redescription mining algorithms, however they are all restricted to use of one or maximally two disjoint sets of attributes (views) to re-describe different subsets of entities. The main reasons for this limitation are computational complexity and potentially large increase in number of produced patterns, in multi-view setting, during redescription mining. In this work we present an algorithm that allows mining redescriptions from multiple views using the CLUS-RM algorithm. Presented algorithm efficiently solves aforementioned problems. Its computational complexity, with respect to attribute operations, increases linearly with the increase of number of views and we present techniques to handle large number of produced redescriptions during redescription mining step.



The authors acknowledge the European Commission’s support through the projects MAESTRA (Gr. no. 612944) and HBP SGA2 (Gr. no. 785907), support of the Croatian Science Foundation (Pr. no. 9623: Machine Learning Algorithms for Insightful Analysis of Complex Data Structures) and partial support by the European Regional Development Fund under the grant KK. (DATACROSS).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Matej Mihelčić
    • 1
    Email author
  • Sašo Džeroski
    • 2
  • Tomislav Šmuc
    • 1
  1. 1.Ruđer Bošković InstituteZagrebCroatia
  2. 2.Jožef Stefan InstituteLjubljanaSlovenia

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