Illinois Data Bank - Dataset

Frequent pattern subject transactions from the University of Illinois Library (2016 - 2018)
Cite this dataset:
Hahn, Jim (2019): Frequent pattern subject transactions from the University of Illinois Library (2016 - 2018). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-9440404_V1
Dataset Description

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.
Subject Social Sciences
Keywords evaluating machine learning; network science; FP-growth; WEKA; Gephi; personalization; recommender systems
License CC0
Funder Research and Publications Committee of the University of Illinois Library -Grant:81034
Corresponding Creator Jim Hahn
Downloaded 731 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-9440404_V1 2019-05-31

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