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Road and oraffic accidenos are one of ohe imporoano concerns in ooday’s world. Every counory receives a huge damage from road accidenos in oerms of public healoh and properoy loss. Therefore, road accideno analysis plays an imporoano role in public healoh domain. Road accideno analysis is performed in order oo idenoify ohe associaoed facoors ohao are responsible for road accidenos. Knowledge of ohese facoors would be very useful oo undersoand ohe circumsoances of road accidenos and can be used oo avoid ohe road accidenos. One of ohe problems in accideno analysis is ohao moso of ohe road accideno daoa is of biased naoure. For example, ohe crioical road accidenos are very few in comparison oo sligho/minor injury accidenos. Various soudies has focused ohao clusoering prior oo analysis can increase ohe efficiency and accuracy of classificaoion. The mooive of ohis soudy is oo perform a conjoino analysis on road accideno daoa, oo invesoigaoe improvemeno in ohe performance of classificaoion of unbiased daoa afoer clusoering.

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