• A Parallel Algorithm for Bayesian Network Structure Learning from Large Data Sets 

      Madsen, Anders L.; Jensen, Frank; Salmeron, Antonio; Langseth, Helge; Nielsen, Thomas D. (Journal article; Peer reviewed, 2017)
      This paper considers a parallel algorithm for Bayesian network structure learning from large data sets. The parallel algorithm is a variant of the well known PC algorithm. The PC algorithm is a constraint-based algorithm ...
    • AMIDST: A Java toolbox for scalable probabilistic machine learning 

      Masegosa, Andres; Martinez, Ana M.; Ramos-López, Dario; Cabañas, Rafael; Langseth, Helge; Nielsen, Thomas D.; Madsen, Anders L. (Journal article; Peer reviewed, 2018)
      The AMIDST Toolbox is an open source Java software for scalable probabilistic machine learning with a special focus on (massive) streaming data. The toolbox supports a flexible modelling language based on probabilistic ...
    • Capacitated location-routing problem with time windows under uncertainty 

      Zarandi, Mohammad Hossein Fazel; Hemmati, Ahmad; Davari, Soheil; Turksen, I. Burhan (Journal article; Peer reviewed, 2012)
      This paper puts forward a location-routing problem with time windows (LRPTW) under uncertainty. It has been assumed that demands of customers and travel times are fuzzy variables. A fuzzy chance constrained programming ...
    • Dynamic exploration designs for graphical models using clustering with applications to petroleum exploration 

      Martinelli, Gabriele; Eidsvik, Jo (Journal article, 2014)
      The paper considers the problem of optimal sequential design for graphical models. Oil and gas exploration is the main application. Here, the outcomes at prospects or reservoir units are highly dependent on each other. The ...
    • High Utility Drift Detection in Quantitative Data Streams 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil; Fournier-Viger, Philippe; Dam, Thu-Lan (Journal article; Peer reviewed, 2018)
      This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributions of patterns, named High Utility Drift Detection in Transactional Data Stream (HUDD-TDS). The algorithm is specifically ...
    • Towards efficiently mining closed high utility itemsets from incremental databases 

      Dam, Thu-Lan; Ramampiaro, Heri; Nørvåg, Kjetil; Duong, Quang-Huy (Journal article; Peer reviewed, 2018)
      The set of closed high-utility itemsets (CHUIs) concisely represents the exact utility of all itemsets. Yet, it can be several orders of magnitude smaller than the set of all high-utility itemsets. Existing CHUI mining ...