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Annals of Computer Science and Information Systems, Volume 10

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

A Conjoint Analysis of Road Accident Data using K-modes Clustering and Bayesian Networks (Road Accident Analysis using clustering and classification)

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DOI: http://dx.doi.org/10.15439/2017R44

Citation: Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering, Vijender Kumar Solanki, Vijay Bhasker Semwal, Rubén González Crespo, Vishwanath Bijalwan (eds). ACSIS, Vol. 10, pages 53–56 ()

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Abstract. Road and traffic accidents are one of the important concerns in today's world. Every country receives a huge damage from road accidents in terms of public health and property loss. Therefore, road accident analysis plays an important role in public health domain. Road accident analysis is performed in order to identify the associated factors that are responsible for road accidents. Knowledge of these factors would be very useful to understand the circumstances of road accidents and can be used to avoid the road accidents. One of the problems in accident analysis is that most of the road accident data is of biased nature. For example, the critical road accidents are very few in comparison to slight/minor injury accidents. Various studies has focused that clustering prior to analysis can increase the efficiency and accuracy of classification. The motive of this study is to perform a conjoint analysis on road accident data, to investigate improvement in the performance of classification of unbiased data after clustering.

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