The paper 1 describes the associationrule mining, its classifications and the atmospheric components like roadwaysurface, climate, and light condition do not strongly influence the fatal accidentrate. But the human factors like being alcoholic or not, and the impact havestrongly affect on the fatal accident rate.  Acommon mechanism to recognize the relations between the data stored in hugedatabase and plays a very significant role in repeated object set mining isassociation rule mining algorithm.

A classical association rule mining methodis the Apriori algorithm whose main aim is to identify repeated object sets toanalyze the roadway traffic data. Classification in data mining methodology focusat building a classifier model from a training data set that is used toclassify records of unrevealed class labels. The Naïve Bayes technique is oneof the probability-based methods for classification and is based on the Bayes’hypothesis with the probability of self-rule between every set of variables. The author applies statisticsanalysis and Fatal Accident Reporting System (FARS) to solve this problem. Fromthe clustering result some regions have larger fatal rate but some others havesmaller.

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When driving within those risky or dangerous states, people take moreattention. When the task performed, data seems never to be sufficient to make astrong choice. If non-fatal accident data, weather condition data, mileagedata, and so on are available, more test could be executed thus more advicecould be made from the data.In paper 2, K-modes clusteringtechnique is a framework that is used as an initial work for division ofdifferent road accidents on road network. Then association rule mining are usedto recognize the various situations that are related with the occurrence of anaccident for the entire data set (EDS) and the clusters recognized by K-modesclustering algorithm. Six clusters (C1toC6) are used based on propertiesaccident type, road type, lightning on road and road feature identified by Kmodes clustering method.

On each cluster association rule mining is applied aswell as on EDS to create rules. Powerful methods with higher raise values are takenfor the inspection. Rules for various clusters disclose the situations relatedwith the accidents within that cluster. These rules are compared with the rulescreated for the EDS and resemblance shows that association rules for EDS does notdisclose correct data that can be related with an accident.

If more feature arepresented large information can be identified that is associated with anaccident. To buildup our methodology, we also performed analysis of all clustersand EDS on monthly or hourly basis. The results of analysis assist methodologythat performing clustering prior to analysis helps to identify better anduseful results that cannot obtained without using cluster analysis.