Applying Association Rules Mining Algorithms for Traffic Accidents in Dubai
Amira A. El Tayeb1, Vikas Pareek2, Abdelaziz Araar3
1Eng. Amira A. El Tayeb, PhD. Scholar, Department of Computer Science, Banasthali University, Rajasthan, India
2Dr. Vikas Pareek, Assoc. Prof., Department of Computer Science, Banasthali University, Rajasthan State, India.
3Dr. Abdelaziz Araar, Assoc. Prof., College of Information Technology, Ajman University, Ajman, UAE.
Manuscript received on August 16, 2015. | Revised Manuscript received on August 29, 2015. | Manuscript published on September 05, 2015. | PP: 1-12 | Volume-5 Issue-4, September 2015 . | Retrieval Number: D2679095415/2015©BEIESP
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©The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Association rule mining algorithms are widely used to find all rules in the database satisfying some minimum support and minimum confidence constraints. In order to decrease the number of generated rules, the adaptation of the association rule mining algorithm to mine only a particular subset of association rules where the classification class attribute is assigned to the right-hand-side was investigated in past research. In this research, a dataset about traffic accidents was collected from Dubai Traffic Department, UAE. After data preprocessing, Apriori and Predictive Apriori association rules algorithms were applied to the dataset in order to explore the link between recorded accidents’ factors to accident severity in Dubai. Two sets of class association rules were generated using the two algorithms and summarized to get the most interesting rules using technical measures. Empirical results showed that the class association rules generated by Apriori algorithm were more effective than those generated by Predictive Apriori algorithm. More associations between accident factors and accident severity level were explored when applying Apriori algorithm.
Keywords: Association Rule Mining, Apriori, Predictive Apriori, Dubai Traffic Accidents.