Performance evaluation of various data mining algorithms on road traffic accident dataset

Abstract

Many researchers use to spend much of time searching for the best performing data mining classification and clustering algorithms to apply in road accident data set for prediction of some classes such causes of the accident, prone locations and time of the accident, even type of the vehicle used to involve in the accident. The study was carried out by using two data mining tools—Weka and Orange. The study evaluated Multi-layer Perceptron, J48, BayesNet classifiers on 150 instances of accident dataset using Weka. The results showed that Multi-layer Perceptron classifier performed well with 85.33% accuracy, followed by J48 with 78.66% accuracy and BayesNet had 80.66% accuracy. The study had also found two best rules for association rule mining using Apriori algorithm with 1.0 minimum supports and 1.27 minimum confidences for rule one and 0.91 minimum supports and 1.15 minimum confidences for rule two. With Silhouette score 0.7, clustering and dimensionality reduction techniques K-means and Self-Organizing Maps were also used on the dataset using Orange data mining tool.