A Comparative Analysis of Heterogeneity in Road Accident Data-Using Data Mining Techniques
Road accidents are one of the most imperative factors that affect the untimely death among people and economic loss of public and private property. Road safety is a term associated with the planning and implementing certain strategy to overcome the road and traffic accidents. Road accident data analysis is a very important means to identify various factors associated with road accidents and can help in reducing the accident rate. The heterogeneity of road accident data is a big challenge in road safety analysis. In this, we are making use of latent class clustering (LCC) and k-modes clustering technique on a new road accident data. Initially,the LCC and k-modes clustering technique are applied on road accident data to form different clusters. Frequent Pattern (FP) growth technique is applied on the clusters formed and entire dataset (EDS). However, in this certain techniques these are well suited to remove heterogeneity of road accident data. The generated results for each cluster and EDS proves that heterogeneity exists in the entire dataset and clustering prior to analysis certainly reduces heterogeneity from the dataset and provides better solutions.
Author Name: T. Ram Prasanth, V. Spanglar Diaz, N. Surendran, V. Udhayavel, Dr.C. Anand and N. Vasuki
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College: K.S. Rangasamy College of Technology, Tiruchengode
Keywords: Road Accident Analysis, Heterogeneity, Data mining, Clustering, FP Growth