Large-Scale Closed High-Utility Itemset Mining
Jerry Chun‐Wei Lin, Youcef Djenouri, Gautam Srivastava, Jimmy Ming‐Tai Wu · 2021 International Conference on Data Mining Workshops (ICDMW) · 2021
An information fusion architecture for large-scale integration of closed high-utility patterns from several distributed databases is presented in this paper. The generic and standard composite model is then initialized to group transactions based on their relevant correlation, which will help assure the accuracy and completeness of our fusion model. The technique uses the well-known MapReduce architecture to handle large-scale datasets for information fusion and integration. According to the results of examination, the developed model is well-suited for managing big databases with little memory consumption.