DM Algorithms Based Clustering for Road Accident Data Analysis
DOI:
https://doi.org/10.26438/ijcse/v6i9.160167Keywords:
Road accident dataset, parallel frequent mining, WekaAbstract
Road accidents due to traffic are ever more being acknowledged as major problem for transportation agencies as well as general people. A substantial unexpected result of transportation systems is road accidents with injuries and loss of lives. In this scenario purpose safe driving, specific study of road traffic data is severe to find out elements that are connected to mortal accidents. In this research paper, we determine factors behind road traffic accidents issues solving by data mining algorithms together with data mining algorithms like Density-based spatial clustering of applications with noise and Parallel Frequent mining. We primarily separate the accident locations into k clusters depend on their accident frequency with Density-based spatial clustering of applications with noise 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 Density-based spatial clustering of applications with noise and parallel frequent mining explores the accidents data with patterns and expect future attitude and efficient accord to be taken to decrease accidents.
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