Acta Marisiensis.
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Anul 2024
Volum 21 (XXXVIII), nr 1 Volum 21 (XXXVIII), nr 2 Anul 2023 Volum 20 (XXXVII), nr 1 Volum 20 (XXXVII), nr 2 Anul 2022 Volum 19 (XXXVI), nr 1 Volum 19 (XXXVI), nr 2 Anul 2021 Volum 18 (XXXV), nr 1 Volum 18 (XXXV), nr 2 Anul 2020 Volum 17 (XXXIV), nr 1 Volum 17 (XXXIV), nr 2 Anul 2019 Volum 16 (XXXIII), nr 1 Volum 16 (XXXIII), nr 2 Anul 2018 Volum 15 (XXXII), nr 1 Volum 15 (XXXII), nr 2 Anul 2017 Volum 14 (XXXI), nr 1 Volum 14 (XXXI), nr 2 Anul 2016 Volum 13 (XXX), nr 1 Volum 13 (XXX), nr 2 Anul 2015 Volum 12 (XXIX), nr 1 Volum 12 (XXIX), nr 2 Anul 2014 Volum 11 (XXVIII), nr 1 Volum 11 (XXVIII), nr 2 Anul 2013 Volum 10 (XXVII), nr 1 Volum 10 (XXVII), nr 2 Anul 2012 Volum 9 (XXVI), nr 1 Volum 9 (XXVI), nr 2 Anul 2011 Volum 8 (XXV), nr 1 Volum 8 (XXV), nr 2 Anul 2010 Volum 7 (XXIV), nr 1 Volum 7 (XXIV), nr 2 Anul 2009 Volum 6 (XXIII) |
2023, Volume 20 (XXXVII), no 1
Avram CALIN, Adrian GLIGOR, Victoria NYLAS, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, ROMANIA Roman DUMITRU, Sintef, Forskningsveien, Norway Abstract: Recently, medical databases have expanded rapidly, and the amount of information is huge. This abundance of data appears as a consequence of the new technologies that have been developed in the medical field and that allow easy data collection. The performance of the technique depends on the input data and available resources. Whereas, in Eclat the repeated scanning of the database is eliminated and consumes less time and we can conclude that Eclat is better than Apriori and Fpgrowth. If we refer to the execution time and memory usage, then the FP-Growth algorithm is more efficient than the Eclat algorithm or the Apriori algorithm. If we consider factor other than time, the result may vary from one factor to another. DOI: https://doi.org/10.2478/amset-2023-0006 Pages: 32-36 Cite as: download info as bibtex View full article |
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Update: 18-Dec-2024 | © Published by University Press |