Research Article
Data mining for road accident analysis in a big data context
@INPROCEEDINGS{10.4108/eai.24-4-2019.2284124, author={Fatima Zahra El Mazouri and Mohammed Chaouki Abounaima and Said Najah and Khalid Zenkouar}, title={Data mining for road accident analysis in a big data context}, proceedings={Proceedings of the Third International Conference on Computing and Wireless Communication Systems, ICCWCS 2019, April 24-25, 2019, Faculty of Sciences, Ibn Tofa\~{n}l University -K\^{e}nitra- Morocco}, publisher={EAI}, proceedings_a={ICCWCS}, year={2019}, month={5}, keywords={data mining association rules fp-growth big data apache spark road accident}, doi={10.4108/eai.24-4-2019.2284124} }- Fatima Zahra El Mazouri
Mohammed Chaouki Abounaima
Said Najah
Khalid Zenkouar
Year: 2019
Data mining for road accident analysis in a big data context
ICCWCS
EAI
DOI: 10.4108/eai.24-4-2019.2284124
Abstract
Data Mining techniques and extracting association rules from a big dataset play an interesting role in knowledge discovery. Therefore, the decision makers encounter a huge number of resulting association rules that can make them unable to choose and decide rationally between these different extracted rules, also the time of the generation of these association rules brings a new challenge, we propose to overcome these challenges a learning model based on FP-growth algorithm using Apache Spark framework, in order to analyze data and extract interesting association rules by taking into account some quality measures. Experimental results on road accident data in France show that the proposed approach can provide useful information that could help the decision makers to choose the appropriate strategies in the perspective of improving road safety.
