Abstract
Now days, road accidents due to traffic are increasingly being recognized as key issue for transportation agencies as well as common people. A considerable unexpected output of transportation systems is road accidents with injuries and loss of lives. In order to suggest safe driving, precise study of road traffic data is serious to discover elements that are related to mortal accidents. In this research paper, we discover factors behind road traffic accidents problem solving by data mining algorithms together with DBSCAN and Parallel Frequent mining algorithm. We initially divide the accident places into k clusters depends on their accident frequency with DBSCAN algorithm. Next, parallel frequent mining algorithm is apply on these clusters to disclose the association between dissimilar attributes in the traffic accident data for realize the features of these places and analyzing in advance them to spot different factors that affect the road accidents in different locations. The main objective of accident data is to recognize the key issues in the area of road safety. The efficiency of prevention accidents based on consistency of the composed and predictable road accident data using with appropriate methods. Road accident dataset is used and implementation is carried by using Weka tool. The outcomes expose that the combination of DBSCAN and parallel frequent mining explores the accidents data with patterns and expect future attitude and efficient accord to be taken to decrease accidents. (C) 2018 The Authors. Published by IASE.