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One of the key targets in twist of fate information analysis is to become aware of the primary factors related to an avenue traffic coincidence. However, heterogeneous nature of street twist of fate data results in the evaluation tough. Records has been used extensively to triumph over this heterogeneity of the twist of fate facts. So as to conquer heterogeneity we propose a technique to categorize the twist of fate information into different classes. Herewe use clustering as a preliminary mission for segmentation of 21,436 road injuries on road community of united kingdom between 2009 and 2016 (each inclusive). On clustered records affiliation rule mining method (apriori algorithm) is carried out to become aware of the semantics of coincidence incidence and to quantify quantity of accident occurrences based on type of vehicle, weather conditions, lighting fixtures.