Frequent pattern subject transactions from the University of Illinois Library (2016 - 2018)

  • James F Hahn (Creator)



The data are provided to illustrate methods in evaluating systematic transactional data reuse in machine learning. A library account-based recommender system was developed using machine learning processing over transactional data of 383,828 transactions (or check-outs) sourced from a large multi-unit research library. The machine learning process utilized the FP-growth algorithm over the subject metadata associated with physical items that were checked-out together in the library. The purpose of this research is to evaluate the results of systematic transactional data reuse in machine learning. The analysis herein contains a large-scale network visualization of 180,441 subject association rules and corresponding node metrics.
Date made availableMay 31 2019
PublisherUniversity of Illinois Urbana-Champaign


  • evaluating machine learning
  • Gephi
  • personalization
  • recommender systems
  • WEKA
  • network science
  • FP-growth

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