Augmenting Classifiers Performance through Clustering: A Comparative Study on Road Accident Data

Sachin Kumar, Prayag Tiwari and Kalitin Vladimirovich Denis
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Sachin Kumar: Graphic Era University, Dehradun, India
Prayag Tiwari: National University of Science and Technology MISIS, Moscow, Russia
Kalitin Vladimirovich Denis: National University of Science and Technology MISIS, Moscow, Russia

International Journal of Information Retrieval Research (IJIRR), 2018, vol. 8, issue 1, 57-68

Abstract: Road and traffic accident data analysis are one of the prime interests in the present era. It does not only relate to the public health and safety concern but also associated with using latest techniques from different domains such as data mining, statistics, machine learning. Road and traffic accident data have different nature in comparison to other real-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district. Further, Naïve Bayes (NB), random forest (RF) and support vector machine (SVM) classification techniques are used to classify the data based on the severity of road accidents. The experiments validate that the LCC technique is more suitable to generate good clusters to achieve maximum classification accuracy.

Date: 2018
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