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DM-MCDA: A web-based platform for data mining and multiple criteria decision analysis: A case study on road accident
Umeå University, Faculty of Science and Technology, Department of Computing Science. (DDM)
Cadi Ayyad university. (LISI)
2019 (English)In: SoftwareX, E-ISSN 2352-7110, Vol. 10, article id 100323Article in journal (Refereed) Published
Abstract [en]

Today’s ultra-connected world is generating a huge amount of data stored in databases and cloud environment especially in the era of transportation. These databases need to be processed and analyzed to extract useful information and present it as a valid element for transportation managers for further use, such as road safety, shipping delays, and shipping optimization. The potential of data mining algorithms is largely untapped, this paper shows large-scale techniques such as associations rule analysis, multiple criteria analysis, and time series to improve road safety by identifying hot-spots in advance and giving chance to drivers to avoid the dangers. Indeed, we proposed a framework DM-MCDA based on association rules mining as a preliminary task to extract relationships between variables related to a road accident, and then integrate multiple criteria analysis to help decision-makers to make their choice of the most relevant rules. The developed system is flexible and allows intuitive creation and execution of different algorithms for an extensive range of road traffic topics. DM-MCDA can be expanded with new topics on demand, rendering knowledge extraction more robust and provide meaningful information that could help in developing suitable policies for decision-makers.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 10, article id 100323
Keywords [en]
data mining, association rules, Multiple criteria decision analysis
National Category
Computer Systems
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:umu:diva-165127DOI: 10.1016/j.softx.2019.100323Scopus ID: 2-s2.0-85071883425OAI: oai:DiVA.org:umu-165127DiVA, id: diva2:1369186
Available from: 2019-11-11 Created: 2019-11-11 Last updated: 2019-11-12Bibliographically approved

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