<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Efficient Algorithms for Patterns Identification in Medical Data

George Emil Palade University of Medicine

Acta Marisiensis. Seria Technologica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Efficient Algorithms for Patterns Identification in Medical Data"}

Recently, medical databases have expanded rapidly, and the amount of information is huge. This abundance of data appears as a consequence of the new...

Efficient Algorithms for Patterns Identification in Medical Data

Online veröffentlicht: 09. Juni 2023

Seitenbereich: 32 - 36

DOI: https://doi.org/10.2478/amset-2023-0006

Schlüsselwörter
big data, Eclat algorithm, Apriori algorithm, FP Growth Algorithm, Hopfield Algorithm

© 2023 Avram Calin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Efficient Algorithms for Patterns Identification in Medical Data

Online veröffentlicht: 09. Juni 2023

Seitenbereich: 32 - 36

DOI: https://doi.org/10.2478/amset-2023-0006

Schlüsselwörterbig data, Eclat algorithm, Apriori algorithm, FP Growth Algorithm, Hopfield Algorithm

© 2023 Avram Calin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
big data, Eclat algorithm, Apriori algorithm, FP Growth Algorithm, Hopfield Algorithm