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Title:Poster for: Approaches to systematic transaction data reuse: machine learning support of information discovery
Author(s):Hahn, James F.
Subject(s):machine learning
network science
WEKA, Gephi
recommender systems
Abstract:The purpose of this poster is to detail approaches in systematic transactional data reuse using machine learning and network science visualization methods. Preliminary machine learning workflows were undertaken in WEKA and made use of the Fp-growth algorithm for seeding a recommender system for library user accounts. The resulting tables of consequent association rules are stored in a production relational database server accessed through the recommender app’s middleware. The association rule database is used at runtime for the machine learning based recommender system. As a result of the availability of databases with association rules, researchers further analyzed their properties using network analysis software were the edges of the graph are the consequent topics and the nodes are the antecedent topics. Network science can be particularly valuable in assisting the understanding of machine learning outputs by visualizing and analyzing graphs the distribution of the topic network. Several key network science metrics, including the average degree, diameter, average clustering coefficient, and total triangles were computed by way of Gephi network analytic tools.
Issue Date:2019-05-13
Citation Info:Hahn J. Approaches to systematic transaction data reuse: machine learning support of information discovery [version 1; not peer reviewed]. F1000Research 2019, 8:657 (poster) (
Genre:Conference Poster
Sponsor:University of Illinois Library, Research and Publications Committee Award 81034
Rights Information:© Jim Hahn 2019
This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0)
Date Available in IDEALS:2019-05-16

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