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Pragmatic Approach to Association Rule Learning in Real-World Scenarios
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.
Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT.ORCID iD: 0000-0003-0412-6199
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a pragmatic approach for designing an efficient tool for extracting knowledge from customer data in the retail industry, e.g. market basket analysis. Association rule learning is an established topic within data mining and knowledge discovery with a large interest from the business intelligence community. With a focus on properties from a real-world environment and with an aim to get customer insights on a cross-hierarchy level, we have chosen to build upon the common Apriori algorithm. This algorithm has been optimized for the chosen real-world environment and adapted for implementation on commonly available computing platforms and workstations using the Microsoft .net framework. Several parallelization strategies have been developed and experimental results indicate that a significant speed-up is possible and that the tool can be utilized for producing valuable information.

Place, publisher, year, edition, pages
IEEE, 2015.
National Category
Computer Sciences
Research subject
Business and IT
Identifiers
URN: urn:nbn:se:hj:diva-38078DOI: 10.1109/CSCI.2015.87OAI: oai:DiVA.org:hj-38078DiVA, id: diva2:1163367
Conference
The 2015 International Conference on Computational Science and Computational Intelligence (CSCI'15)
Available from: 2016-01-14 Created: 2017-12-06 Last updated: 2018-09-11Bibliographically approved

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Johansson, Ulf

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  • apa
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  • ieee
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Language
  • de-DE
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  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • text
  • asciidoc
  • rtf