Abstract
Finding significant changes in multidimensional data is a crucial task for several use cases. With change mining on frequent itemsets, alterations in the statistics of personal injury road accidents can be detected. Since a lot of factors determine the situation of an accident, the statistical data consists of too many dimensions to detect changes in their frequencies manually. Hence, we propose an automated approach based on Frequent Itemset Mining using known algorithms, such as Apriori, to detect significant changes within the data. Firstly, the 1.8 million records of Great Britain’s road safety data openly published by the Department for Transport are prepared and adjusted to match the algorithms’ requirements. Secondly, the most frequent itemsets for each time interval are filed and compared to the itemsets of the previous period and are split into classes of changing, stable and semi-stable itemsets. To build our framework we concentrate on the support of frequent itemsets and the changes between the months of two consecutive years.
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Meißner, K., Rüther, C., Ambrosi, K. (2018). Detecting Changes in Statistics of Road Accidents to Enhance Road Safety. In: Kliewer, N., Ehmke, J., Borndörfer, R. (eds) Operations Research Proceedings 2017. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-89920-6_10
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DOI: https://doi.org/10.1007/978-3-319-89920-6_10
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